Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria

Deep learning neural network development for the... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/13-981" }, "headline": "Deep learning neural network development for the classification of bacteriocin sequences produced by lactic...", "datePublished": "2024-08-30T11:18:40", "dateModified": "2025-06-20T13:51:27", "author": [ { "@type": "Person", "name": "Lady L. González" }, { "@type": "Person", "name": "Isaac Arias-Serrano" }, { "@type": "Person", "name": "Fernando Villalba-Meneses" }, { "@type": "Person", "name": "Paulo Navas-Boada" }, { "@type": "Person", "name": "Jonathan Cruz-Varela" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background The rise of antibiotic-resistant bacteria presents a pressing need for exploring new natural compounds with innovative mechanisms to replace existing antibiotics. Bacteriocins offer promising alternatives for developing therapeutic and preventive strategies in livestock, aquaculture, and human health. Specifically, those produced by LAB are recognized as GRAS and QPS. Methods In this study was used a deep learning neural network for binary classification of bacteriocin amino acid sequences, distinguishing those produced by LAB. The features were extracted using the k-mer method and vector embedding. Ten different groups were tested, combining embedding vectors and k-mers: EV, ‘EV+3-mers’, ‘EV+5-mers’, ‘EV+7-mers’, ‘EV+15-mers’, ‘EV+20-mers’, ‘EV+3-mers+5-mers’, ‘EV+3-mers+7-mers’, ‘EV+5-mers+7-mers’, and ‘EV+15-mers+20-mers’. Results Five sets of 100 characteristic k-mers unique to bacteriocins produced by LAB were obtained for values of k = 3, 5, 7, 15, and 20. Significant difference was observed between using only and concatenation. Specially, ‘5-mers+7-mers+EV ’ group showed superior accuracy and loss results. Employing k-fold cross-validation with k=30, the average results for loss, accuracy, precision, recall, and F1 score were 9.90%, 90.14%, 90.30%, 90.10%, and 90.10% respectively. Folder 22 stood out with 8.50% loss, 91.47% accuracy, and 91.00% precision, recall, and F1 score. Conclusions The model developed in this study achieved consistent results with those seen in the reviewed literature. It outperformed some studies by 3-10%. The lists of characteristic k-mers pave the way to identify new bacteriocins that could be valuable for therapeutic and preventive strategies within the livestock, aquaculture industries, and potentially in human health. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-981/v1", "name": "Deep learning neural network development for the classification of..." } } ] } Home Browse Deep learning neural network development for the classification of... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article González LL, Arias-Serrano I, Villalba-Meneses F et al. Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.12688/f1000research.154432.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] Lady L. González https://orcid.org/0000-0002-6302-4072 1 , Isaac Arias-Serrano https://orcid.org/0000-0002-1877-3648 1 , Fernando Villalba-Meneses https://orcid.org/0000-0002-7236-7499 1 , Paulo Navas-Boada https://orcid.org/0000-0003-3239-7375 1 , Jonathan Cruz-Varela https://orcid.org/0000-0001-5547-3487 1 Lady L. González https://orcid.org/0000-0002-6302-4072 1 , Isaac Arias-Serrano https://orcid.org/0000-0002-1877-3648 1 , [...] Fernando Villalba-Meneses https://orcid.org/0000-0002-7236-7499 1 , Paulo Navas-Boada https://orcid.org/0000-0003-3239-7375 1 , Jonathan Cruz-Varela https://orcid.org/0000-0001-5547-3487 1 PUBLISHED 30 Aug 2024 Author details Author details 1 School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, Provincia de Imbabura, 100119, Ecuador Lady L. González Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Isaac Arias-Serrano Roles: Supervision, Validation, Visualization, Writing – Review & Editing Fernando Villalba-Meneses Roles: Resources, Software, Validation, Visualization Paulo Navas-Boada Roles: Validation, Visualization, Writing – Review & Editing Jonathan Cruz-Varela Roles: Conceptualization, Methodology, Project Administration, Software, Supervision, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Bioinformatics gateway. Abstract Background The rise of antibiotic-resistant bacteria presents a pressing need for exploring new natural compounds with innovative mechanisms to replace existing antibiotics. Bacteriocins offer promising alternatives for developing therapeutic and preventive strategies in livestock, aquaculture, and human health. Specifically, those produced by LAB are recognized as GRAS and QPS. Methods In this study was used a deep learning neural network for binary classification of bacteriocin amino acid sequences, distinguishing those produced by LAB. The features were extracted using the k-mer method and vector embedding. Ten different groups were tested, combining embedding vectors and k-mers: EV, ‘EV+3-mers’, ‘EV+5-mers’, ‘EV+7-mers’, ‘EV+15-mers’, ‘EV+20-mers’, ‘EV+3-mers+5-mers’, ‘EV+3-mers+7-mers’, ‘EV+5-mers+7-mers’, and ‘EV+15-mers+20-mers’. Results Five sets of 100 characteristic k-mers unique to bacteriocins produced by LAB were obtained for values of k = 3, 5, 7, 15, and 20. Significant difference was observed between using only and concatenation. Specially, ‘5-mers+7-mers+EV ’ group showed superior accuracy and loss results. Employing k-fold cross-validation with k=30, the average results for loss, accuracy, precision, recall, and F1 score were 9.90%, 90.14%, 90.30%, 90.10%, and 90.10% respectively. Folder 22 stood out with 8.50% loss, 91.47% accuracy, and 91.00% precision, recall, and F1 score. Conclusions The model developed in this study achieved consistent results with those seen in the reviewed literature. It outperformed some studies by 3-10%. The lists of characteristic k-mers pave the way to identify new bacteriocins that could be valuable for therapeutic and preventive strategies within the livestock, aquaculture industries, and potentially in human health. READ ALL READ LESS Keywords Deep Learning Neural Network, Bacteriocin, Lactic Acid Bacteria , K-mers, Embedding Vectors Corresponding Author(s) Lady L. González ( [email protected] ) Isaac Arias-Serrano ( [email protected] ) Jonathan Cruz-Varela ( [email protected] ) Close Corresponding authors: Lady L. González, Isaac Arias-Serrano, Jonathan Cruz-Varela Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 González LL et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: González LL, Arias-Serrano I, Villalba-Meneses F et al. Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.12688/f1000research.154432.1 ) First published: 30 Aug 2024, 13 :981 ( https://doi.org/10.12688/f1000research.154432.1 ) Latest published: 20 Jun 2025, 13 :981 ( https://doi.org/10.12688/f1000research.154432.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction The emergence of antibiotic-resistant bacteria and the rise of new diseases are critical challenges that demand the search for new natural compounds with innovative mechanisms of action to support or replace current antibiotics in use. 1 , 2 Some bacteria have the ability to produce antimicrobial proteins to inhibit or kill other nearby bacteria. This serves as a form of microbial competition and defense. 3 These antimicrobial proteins, known as bacteriocins, are effective against related or similar bacteria to those that produce them, but generally do not affect other organisms such as human or animal cells. 4 , 5 Bacteriocins have emerged as alternatives for treating urinary tract, skin, respiratory, gastrointestinal infections, among others. They provide additional or alternative treatment options compared to conventional antibiotics. 6 – 8 A summary of the classification of bacteriocins can be seen in Table 1 . Table 1. Classification and characteristics of bacteriocins. This table summarizes the different classes of bacteriocins, detailing their molecular mass, properties, structural characteristics, and examples. Classification Characteristics Examples Reference Class I (lantibiotics) Subclass Ia Subclass Ib Molecular mass : < 5 kDa. Properties: resistant to proteolysis, thermostable, and resistant to pH. Structure: intramolecular cyclic, providing rigidity and resistance to the action of proteases. Nisin, Subtilin, Mersacidin 9 – 13 Class II (non- antibiotics) Subclass IIa Subclass IIb Subclass IIc Subclass IId Molecular mass : < 10 kDa. Properties : thermostable, pH resistant, and ability to depolarize bacterial cell membranes. Structure : amphipathic helical with disulfide bridges that increase the stability of the peptide. Pediciona, Plantaricin, Lactococcin A 9 – 12 , 14 Class III Subclass IIIa Subclass IIIb Molecular mass : > 30 kDa. Properties : thermolabile, and unmodified. They have two mechanisms of action: lytic and non-lytic. Structure : large proteins. Helviticin J, Millericin B 9 , 10 , 14 Class IV - Molecular mass : - Properties : thermostable, and resistant to pH. Structure : large peptides with complex structure. Lactocin S, Eenterocin AS-48, Circularin 10 A common type of bacteria known to produce bacteriocins is Lactic Acid Bacteria (LAB). 15 Additionally, LABs are particularly intriguing due to the long history of safe use of some strains and their status as “Generally Recognized as Safe” (GRAS), along with the “Qualified Presumption of Safety” (QPS) that most LAB strains possess. 16 , 17 Typically, LABs are either cocci or rods and encompass over 60 genera. The major genera include Aerococcus, Carnobacterium, Enterococcus, Lactobacillus, Lactococcus, Leuconostoc, Oenococcus, Pediococcus, Streptococcus, Tetragenococcus, Vagococcus, Propionibacterium, Bifidobacterium, and Weisella. 2 , 18 Bacteriocins produced by LAB have gained popularity due to their promising applications in the food industry as natural preservatives. This reduces the need for adding chemical preservatives or applying physical treatments during food production. 19 , 20 Additionally, they can be used within the pharmaceutical and medical industry, serving as therapeutic agents or alternatives to traditional antibiotics. 21 Bacteriocins derived from LABs are colorless, tasteless, and odorless. Moreover, they possess several crucial metabolic traits such as strong tolerance to low pH, the ability to produce acid and aroma, protein hydrolysis, production of viscous exopolysaccharides, and resilience to high thermal stress. 12 , 22 , 23 On the other hand, the development of machine learning and artificial intelligence techniques, coupled with the availability of sequenced bacterial genomes, has enabled the use of new techniques in bioinformatics. In the context of bacteriocins, employing neural networks allows for the identification of patterns in amino acid sequences (aa), providing an advantage in discovering new bacteriocins that remain uncharacterized. 24 , 25 This research is based on the need to efficiently identify bacteriocin sequences produced by LAB, 26 , 27 as the genetic and structural diversity of these peptides poses a challenge. 28 Therefore, a deep learning neural network was developed for the binary classification of bacteriocin amino acid sequences, distinguishing between those produced by lactic acid bacteria (BacLAB) and non-BacLAB. Feature extraction using the k-mer method and vector embedding was employed. Fields where bacteriocins can be applied to address diverse issues Food industry Some microorganisms can cause food and beverage contamination, leading to their deterioration, posing a constant concern in the food industry as it can spoil taste and cause foodborne illnesses in humans. 29 , 30 Bacterial pathogens transmitted through food are the primary cause of food poisoning. Chemical additives have been widely used for food preservation; however, their toxicity may raise human health issues. Some of the commercially used chemical preservatives include various synthetic chemicals. 31 , 32 Currently, there is a negative public perception towards chemical preservatives. This has led to a consumer preference for alternatives considered more “natural”. 33 In response to this demand for natural preservatives, bacteriocins show significant potential for use in the food industry, aiming to prevent food spoilage and hinder disease transmission by inhibiting the growth of pathogenic bacteria. 33 , 34 Certain LAB-derived bacteriocins, such as nisin, pediocin, enterocin, and leucocin, have been employed for this purpose. 35 , 36 They can be used in the preservation of dairy products, meats, vegetables, sourdough bread, wine, among others. 2 Furthermore, using bacteriocins as preservatives leads to the creation of tastier, less acidic, lower salt content, and higher nutritional value food products. Additionally, these bacteriocins can be used as antimicrobial films in food packaging to extend the shelf life and expiration dates of these products. 37 , 38 However, it’s important to note that while bacteriocins are a promising tool, their application is still under development and study, and they do not completely replace traditional antibiotics in all cases. Further research is needed to fully understand their potential and limitations. 33 Medicine Currently, the growing resistance of bacterial pathogens poses a serious challenge to global public health, impacting not only humans but also animals, plants, and the environmental ecosystem. 39 Drug resistance is on the rise worldwide due to the excessive and uncontrolled use of antimicrobial substances. According to the WHO, superbugs represent one of the most significant threats to public health, causing millions of deaths each year. 40 It is projected that by 2060, at least 20 new types of antibiotics will be needed to effectively address the problem of bacterial drug resistance. However, developing new antibiotics involves a long and complex process, posing a significant barrier. Therefore, it is imperative to explore and develop new therapeutic strategies capable of effectively combating antibiotic-resistant microorganisms. 7 , 18 In clinical applications, some bacteriocins have demonstrated efficacy in treating infections, especially those caused by multidrug-resistant strains. Being produced by non-pathogenic bacteria that typically colonize the human body, they are of interest in the medical field. 41 – 43 Some identified bacteriocins applicable in the treatment of infectious diseases include nisin, lacticin, salivaricin, subtilosin, mersacidin, enterocin, gallidermin, epidermin, and fermentin. 29 Furthermore, bacteriocins have been explored for potential use in treating conditions such as diarrhea, dental caries, mastitis, and cancer. 44 , 45 Livestock animal husbandry Livestock, comprising domestic animals raised in agricultural settings, play a crucial role in providing labor and a wide range of products such as milk, meat, eggs, hides, and leather. Maintaining livestock health and improving the economy through optimal production requires proper feeding and effective hygiene practices. However, farm animals remain susceptible to infections caused by viruses and bacteria despite these measures. 46 – 48 In the quest to safeguard animal health on farms, novel techniques are being explored as alternatives to antibiotics. This search becomes especially relevant due to various infectious diseases caused by bacteria in cattle, including conditions like mastitis, post-weaning diarrhea, meningitis, arthritis, endocarditis, pneumonia, and septicemia. Despite this pressing need, the range of bacteriocins evaluated for maintaining livestock health is limited, primarily focusing on nisin, lacticin, garvicin, and macedocin. 49 – 51 The application of bacteriocins in livestock food or water has ensured food safety by reducing the presence of foodborne pathogens in the gastrointestinal tract. 52 , 53 This application of bacteriocins has not only been used to improve the productivity of cattle but also probiotic strains capable of producing bacteriocins have been explored to increase the growth rate of pigs. Furthermore, efforts have been made in the poultry industry to control Salmonella. 54 Maintaining a diet with bacteriocin-producing bacteria can reduce existing populations of foodborne pathogens such as Salmonella and Escherichia coli and prevent the reintroduction of these pathogenic bacteria. 52 Additionally, they can be used in other forms such as the development of intra-mammary formulations for mastitis, which act as germicidal preparations applied to cows’ udders. 55 , 56 Aquaculture Aquatic cultures face similar challenges to livestock, dealing with potential pathogenic risks and requiring preventive measures such as various breeding techniques, vaccination, and antibiotic use. 52 , 57 Bacteriocins function as probiotics, leveraging the interconnected ecosystem shared by animals and microorganisms within the aquatic environment. This interaction promotes probiotic competition against pathogenic bacteria, facilitating the production of inhibitory compounds. As a result, it improves water quality, strengthens the immune response of host species, and enhances species nutrition by producing additional digestive enzymes. 58 – 60 Studies involving photosynthetic bacteria like Rhodobacter sphaeroides and bacteriocins derived from Bacillus spp. have investigated their impact as probiotics on shrimp growth and digestive enzyme activity. 61 , 62 Likewise, experiments with nutrient-enriched water using Alchem Poseidon, a blend of Bacillus subtilis , L. acidophilus, Clostridium butyricum, and Saccharomyces cerevisiae , have shown potential for preventing infections, as the administered bacteria successfully colonized both the host and the aquatic environment. 63 , 64 Work related to artificial intelligence for the classification of bacteriocin sequences Among the works carried out using deep learning neural networks to analyze large datasets and achieve accurate classification of bacteriocins is the article by Poorinmohammad et al. (2018). 65 In this study, peptide sequence analysis is conducted using machine learning alongside feature selection, and a Sequential Minimal Optimization (SMO)-based classifier is developed to predict lantibiotics, achieving precision and specificity values of 88.5% and 94%, respectively. Furthermore, in the work of Yount et al. (2020), 66 the BACII𝛼 algorithm was created to identify and classify bacteriocin sequences. This algorithm integrates a consensus signature sequence, physicochemical elements, and genomic patterns within a high-dimensional query tool to select peptides resembling bacteriocins. It accurately retrieved and distinguished almost all known class II bacteriocin families, achieving a specificity of 86%. In the article by Akhter and Miller (2022), a similar approach was taken, where a machine learning-based software tool was developed to extract potential features from bacteriocin and non-bacteriocin sequences, considering their physicochemical and structural properties. Support Vector Machine (SVM) and Random Forest (RF) algorithms were employed. In this article, a precision of 95.54% was achieved. 67 Various methods have also been used to identify bacteriocins from bacterial genomes based on bacteriocin precursor genes or contextual genes. For instance, BAGEL 68 and BACTIBASE 69 are online tools that analyze experimentally validated and annotated bacteriocins, similar to the BLASTP protein search tool. These tools rely on methods that facilitate the identification of potential bacteriocin sequences based on the homogeneity of known bacteriocins. However, these similarity-based approaches often fail to detect sequences that differ from known sequences, resulting in a significant number of false negatives. This issue led to the development of the BOA software, 70 which attempts to address this problem by integrating prediction tools based on the conservation of contextual genes from the bacteriocin operon. Nevertheless, they still rely on genomic searches based on homology. In addition, the study by Nguyen et al. (2019) utilized a different technique from the previous methods by applying word embeddings of protein sequences to represent bacteriocins. This approach takes into account the amino acid order in protein sequences to predict new bacteriocins from sequences without relying on sequence similarity. This method even enables the prediction of potentially unknown bacteriocins with high probability. Overall, representing sequences with word embeddings that preserve information about the sequence order can be applied to peptide and protein classification problems where sequence similarity cannot be used. 71 Similarly, in the work by Hamid and Friedberg (2019), 72 word embedding was used to identify bacteriocins, representing protein sequences using Word2vec. These representations were used as inputs for various deep recurrent neural networks (RNNs) to distinguish between bacteriocin and non-bacteriocin sequences. This technique addresses challenges such as diversity among bacteriocin sequences. Meanwhile, Fields et al. (2020) developed a process for designing and testing bacteriocin-derived compounds. They employed machine learning and a filter of biophysical features to generate an algorithm that predicts bacteriocins. This involved generating characteristic sequences of 20-mers. 25 Additionally, there are other works that use antimicrobial peptide (AMP) sequences. However, it’s important to note that all bacteriocins are antimicrobial peptides, but not all antimicrobial peptides are bacteriocins. For example, in the study by Li et al. (2022), 73 they present a deep learning model called AMPlify for antimicrobial peptide prediction. The cross-validation results for the model achieve 91.70% accuracy, 91.40% sensitivity, 92.00% specificity, and 91.68% F1 score. Similarly, in Wang et al. (2023), 74 they developed a bidirectional short and long-term memory deep learning network called AMP-EBiLSTM with an accuracy of 92.39%. This approach employs a binary profile function and a pseudo-amino acid composition to capture local sequences and extract amino acid information. In another study, a model known as AMP-BERT was developed. This network uses a bidirectional transformer encoder (BERT) architecture to extract structural and functional information from input peptides, categorizing each input as AMP or non-AMP. Notably, this network achieved a correct prediction rate of 76% for external test sequences selected in this research. 75 Similarly, a system called AMPs-Net was introduced, an algorithm designed to streamline experimentation and improve the efficiency of discovering potent AMPs. It exhibited good prediction of the antibacterial capabilities of numerous peptides, with an average accuracy ranging from 80.98% to 91.2% and precision varying from 75.77% to 94.26%. 76 In the study by Gull et al. (2019), they achieved 97% accuracy for an algorithm that identifies biologically active and antimicrobial peptides. 77 Similarly, in the study by Redshaw et al. (2023), a neural network was developed to predict the antimicrobial activity of sequences. It was trained on two different databases, achieving a precision result of 86-92% for one database and 72-77% for the other. 78 In another work, an application used for predicting antimicrobial peptides based on properties achieved an accuracy exceeding 80% and sensitivity above 90%. 79 In the study by Yan et al. (2020), a method for predicting short-length antimicrobial peptides (≤ 30 aa) is presented. Their convolutional neural network, called Deep-AmPEP30, demonstrated a 77% accuracy rate. 80 Additionally, in the study by Veltri et al. (2018), a deep learning neural network using embedding vectors to reduce weights when processing sequences was developed. It was shown that antimicrobial peptides could be constructed using only nine amino acids, achieved through the k-mers method. The network achieved an accuracy of 90.55%. 81 Methods The general flow of the method used is illustrated in Figure 1 . In section a), the input of the AA sequences is shown. There are two groups: BacLAB and Non-BacLAB. Subsequently, feature extraction is performed for each sequence. Two methods were employed. In b), the use of k-mers to obtain vectors of 0s and 1s representing the presence or absence of representative k-mer groups is shown. The resulting vectors have a length of 100. Meanwhile, in c), a 128-character embedded vector is obtained by passing the sequence through an RNN. These features are concatenated in d). The resulting concatenation serves as input for the DNN in step e). Finally, in f), a prediction of the aa sequences entered into the trained model is made. Figure 1. Methodological workflow for predicting bacteriocin AA sequences. This figure illustrates the comprehensive flow of the method used to predict bacteriocin amino acid sequences in BacLAB and Non-BacLAB groups. Data collection The AA sequences from both BacLAB and Non-BacLAB were obtained using the publicly accessible UniProt database, downloaded in xlsx format using the Excel option on the platform. 82 The search on this platform was conducted using the keyword “bacteriocin.” The retrieved parameters for each bacteriocin include: Entry, Organism, Length, and Sequence. Additionally, considering the binary classification, a column was added to label the sequences. The BacLAB dataset was labeled as 1, while the Non-BacLAB sequences were labeled as 0. To classify which sequences correspond to BacLAB and which ones to Non-BacLAB, the parameter “organism” was considered to identify the species that produce the bacteriocin. The LAB genera included for classification encompassed Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, Streptococcus, Aerococcus, Alloiococcus, Carnobacterium, Dolosigranulum, Enterococcus, Oenococcus, Tetragenococcus, Vagococcus, and Weissella. 83 The decision was made to use sequences with a length of ≥ 50 AA and ≤ 2000. As a result, a total of 24,964 sequences were obtained for the BacLAB dataset. For the Non-BacLAB dataset, after length-based selection, 25,000 sequences were randomly chosen since this dataset had a larger number of sequences. This was done to avoid introducing bias in learning by favoring the class with more data. The distribution of sequences according to their lengths can be observed in Figure 2 . Figure 2. Correlation of the sequence number with the amino acid number. The curves show the correlation between the number of sequences and the number of amino acids of the BacLAB and Non-BacLAB. Feature extraction K-mers In the realm of amino acid sequence processing (or biological sequences in general) using neural networks, a ‘k-mer’ refers to subsequences of length ‘k’. 84 These subsequences are formed by dividing a longer sequence into specific-sized fragments, where ‘k’ represents the size of each fragment. 85 For example, a k-mer of size 5 would involve splitting the sequence into all possible subsequences of length 5, as illustrated in Figure 3 . The k-mer features of a set of sequences enable the discovery of hidden patterns within that sequence population. Additionally, k-mers are useful for representing sequences in a more manageable way. 86 Figure 3. Illustration of k-mers generated from an amino acid sequence with different k-values. On the left side are shown the k-mers that would be obtained from a sequence if k=5 is set. On the right side the same sequence is used, but in this case k=7. At this stage, a list of the 100 most common k-mers within the BacLAB data set was generated. For this, several values of k were selected (k=3, 5, 7, 15, and 20). The k-mers of each BacLAB sequence were generated. Once all the k-mers were obtained, the frequency of each of them was counted. The 100 k-mers with the highest frequency were selected; this was done for each value of k, resulting in five different lists. After compiling the lists, feature vectors of ‘0’ and ‘1’ were extracted for each sequence, both for those in the BacLAB and Non-BacLAB groups. The k-mers obtained from each sequence were compared with the list of k-mers. A ‘1’ was assigned if the listed k-mer was present in the analyzed sequence, while a ‘0’ was assigned if the k-mer was not found. This process produced a vector of length 100. Figure 4 illustrates the process. Figure 4. Feature extraction from an AA sequence. The list of 100 selected k-mers is compared with the k-mers of the input sequence. If one of the k-mers of the sequence is found in the list, '1' is added; if it is not found, a '0' is added. This process generates a representative vector for the sequence with 100 features in length. In this example, k=5 is used. Embedding vectors Word embeddings are numerical representations of amino acids, where each letter denoting an amino acid receives a unique and discrete value. 87 Each protein is treated as a distinct input token, and the set of 20 amino acids forms a specific dictionary. Table 2 shows the abbreviations used to denote each amino acid. Table 2. Amino acids present in nature and their respective abbreviation. This provides a quick reference for the common abbreviations used in biological and biochemical studies. Amino acid Abbreviation (3 letters) Abbreviation (1 letter) Alanine Ala A Arginine Arg R Asparagine Asn N Aspartic Acid Asp D Cysteine Cys C Glutamic Acid Glu E Glutamine Gln Q Glycine Gly G Histidine His H Isoleucine Ile I Leucine Leu L Lysine Lys K Methionine Met M Phenylalanine Phe F Proline Pro P Serine Ser S Threonine Thr T Tryptophan Trp W Tyrosine Tyr Y Valine Val V For example, for ‘A’ (Alanine), the index 1 is assigned. Consequently, in a sequence, each occurrence of ‘A’ is denoted with the value 1. Figure 5 clarifies the process of generating the index vector. If letters were to appear in the sequence that are not found in the list of amino acids, they will be represented as zero. These indices are used to encode sequences before introducing them into the neural network that generates the embedded vectors. Figure 5. Encoding of amino acid sequences. a) The index number corresponding to each aa is assigned. b) Shows how the sequence is encoded with the indices that correspond to each AA. c) Given that there can be letters or numbers in the sequence that do not exist in the aa list, a value of 0 is assigned as an index. This way, errors are avoided when processing the sequence. Once the index-encoded vectors are obtained, the embedding vectors are extracted. To derive these features, a recurrent neural network (RNN) is applied using the Gated Recurrent Unit (GRU) cell. RNNs with GRUs can handle sequences of varying lengths due to their inherent sequential processing nature and the specific architecture of GRUs. This makes GRU-based RNNs particularly useful in applications where sequence lengths are variable, as they can efficiently handle input length variability without losing learning capacity. 88 – 90 The embedding layer in the network acts as a lookup table or a weight matrix where each row represents, in our case, a vectorized representation of a specific amino acid. 91 The number of rows is equal to the count of unique elements in the vocabulary, which is the number of amino acids plus one, including index zero reserved for a non-existent variable in the amino acid list. The number of columns represents the embedding dimension, a model hyperparameter set to 128 in this case. Consequently, the length of the embedding vector obtained is also 128 for each sequence. Normally, before training begins, the weight matrix is initialized randomly along with all the network parameters. However, for this step, a pre-trained network is used, loading the weights into the model. Figure 6 illustrates the structure of the RNN model. Figure 6. Flowchart of RNN model using GRU cell. Concatenated data sets Different datasets will be used to train the neural network and determine which combination of parameters produces the best results. For the selection of k-mers, values of k=3, k=5, k=7, k=15, and k=20 will be used, as shown in Table 3 . Table 3. Parameters for neural network training. This table presents the different k-mer values used for training the neural network. Concatenation groups EV EV + 3-mers EV + 5-mers EV + 7-mers EV + 15-mers EV + 20-mers EV + 3-mers + 5-mers EV + 3-mers + 7-mers EV + 5-mers + 7-mers EV + 15-mers + 20-mers These specific values for k were selected based on findings from existing research that identify characteristic patterns in certain families of bacteriocins. For example, class IIa bacteriocins have a distinct 5 AA motif, either YGNGV or YDNGI, often found at the N-terminal end. 13 , 92 However, other studies describe this characteristic motif as a 7 AA sequence, such as YGNGVXC (where X can be any AA). 93 , 94 The choice of higher k values was influenced by findings that revealed similarities in the N-terminal half of sequences spanning between 17 and 19 AAs. 95 Additionally, a distinctive sequence among bacteriocins is YGNGVXCXXXXCXV, spanning 14 AAs, or alternatively, YGNGVXCXXXXCXVXWXXA, extending to 19 AAs. 96 , 97 This characteristic amino acid pattern is known as the “pediocin box”. 96 Deep neural network To predict amino acid sequences, a Deep Neural Network (DNN) was employed following the structure described in Jeff et al.’s article. 97 This type of network was chosen for its ability to learn complex patterns and representations from data. Additionally, they can efficiently handle large datasets. 98 The construction of this neural network used Python 3.10.12 in Google Colab along with several libraries: i) Pandas (RRID:SCR 018214), 99 ii) Keras, iii) Scikit-learn (RRID:SCR 002577), 100 iv) NumPy (RRID:SCR 008633), 101 and v) Matplotlib. The network architecture consists of four blocks. The input for each sequence is a vector, which corresponds to the concatenation of the results described in the k-mers section and the embedding features. Therefore, the length of the input depends on the number of concatenated features. In Figure 7 , a representation is used where the extracted results using k-mers for k=5 and k=7, and the embedding features are concatenated. Since the result in k-mers corresponds to a vector of length 100, while the embedding features provide a vector length of 128, the input corresponds to a vector length of 328 for each sequence. The output of the neural network is the class of each sequence, where 1 denotes BacLAB and 0 represents non-BacLAB. Figure 7. Flowchart of the deep neural network. The model established the number of neurons in each defined layer block, with 128 neurons for the first two layers, 64 neurons for the next four layers in the second block, followed by 32 neurons for the five subsequent layers in the third block, and finally, two neurons in the last two layers in the fourth block. The number of neurons was determined based on the input parameters and the DNN architecture. 102 Out of the total thirteen layers in the model (excluding input and output layers), four layers are dense, three layers are activation layers, three layers are dropout layers, two layers are normalization layers, and one layer is a flattening layer. Table 4 provides a summary of the layers in the proposed DNN model. Table 4. Layers of the deep neural network model. Layer Type Output shape Param # dense Dense (None, 128) 42112 dropout Dropout (None, 128) 0 dens e 1 Dense (None, 64) 8256 batc h normalization Batch (None, 64) 256 activation Activation (None, 64) 0 dropou t 1 Dropout (None, 64) 0 dens e 2 Dense (None, 32) 2080 batc h normalizati o n 1 Batch (None, 32) 128 activation 1 Activation (None, 32) 0 dropou t 2 Dropout (None, 32) 0 flatten Flatten (None, 32) 0 dens e 3 Dense (None, 2) 66 activatio n 2 Activation (None, 2) 0 Additionally, among the hyperparameters used, 75 epochs were set, a batch size of 40, and a learning rate of 2.5×10-5 for the Adam optimizer. “Mean_absolute_error” was used as the loss function. For training and testing the neural network, the k-fold cross-validation technique was employed, with k=30 selected. Statistics analysis In this study, ANOVA test along with Tukey test was used to assess significant differences among multiple groups based on parameters of interest, including accuracy, loss, precision, recall, and F1 score. These parameters are critical for evaluating the performance of the implemented neural network. A confidence interval of 95% was selected to ensure that the differences identified between the groups are statistically significant, providing greater certainty about the conclusions drawn from the analysis. It is important to note that RStudio Cloud software was used as the statistical analysis tool to conduct these evaluations. Results The lists of k-mers were obtained for values of k=3, k=5, k=7, k=15, and k=20. For each k-mer, the 100 most frequent repetitions among the sequences were selected. The list can be found in a xlsx file in the repository. 103 Through k-fold cross-validation, various performance metrics of the neural network were obtained. These metrics include loss, precision, recall, F1 score, and accuracy. They were evaluated for each group with different feature concatenations. Since thirty iterations were performed for each set, Table 5 presents the metrics averaged per group. Table 5. Performance metrics obtained from k-fold cross validation (k=30) using different concatenation groups. Group Loss Accuracy Precision Recall F1 EV 10.818 89.423 0.897 0.895 0.895 3-mers + EV 11.500 88.648 0.889 0.887 0.887 5-mers + EV 10.000 90.071 0.904 0.902 0.901 7-mers + EV 10.100 90.049 0.903 0.901 0.901 15-mers + EV 10.500 89.584 0.897 0.895 0.895 20-mers + EV 10.300 89.763 0.899 0.898 0.898 3-mers + 5-mers + EV 10.900 89.184 0.893 0.891 0.891 3-mers + 7-mers + EV 11.600 89.085 0.893 0.892 0.892 7-mers + 5-mers + EV 9.900 90.143 0.903 0.901 0.901 15-mers + 20-mers +EV 10.200 89.885 0.900 0.899 0.899 The initial evaluation was conducted using only features extracted from EV. The results obtained for each metric demonstrate notable performance, as both precision and F1 score reached approximately 89%, while the loss function was around 10%. However, an exploration was conducted by including more features to examine if the metric percentages could be improved. Therefore, a concatenation of EV features with various k-mers was implemented. To demonstrate if there are significant differences between the metrics of each group, an Analysis of Variance (ANOVA) was conducted for each metric. Table 6 shows the results obtained. This analysis revealed substantial differences between the groups, as the Pr(>F) values are less than α=0.05. Therefore, the null hypothesis is rejected, and the alternative hypothesis is accepted. Table 6. Resultados de la prueba ANOVA. Los parámetros de la tabla indican: Df: Grados de Libertad, Sum Sq: Suma de cuadrados, Mean Sq: Cuadrado medio, Pr(>F): Valor p. Parametro Factor Df Sum Sq Mean Sq F value Pr( > F) Loss Group 9 102.58 113.978 10.321 8.042e-14 Residuals 290 320.27 11.044 - - Acc Group 9 65.982 73.314 12.524 < 2.2e-16 Residuals 290 169.765 0.5854 - - Precision Group 9 0.0066813 0.00074237 12.326 < 2.2e-16 Residuals 290 0.0174667 0.00006023 - - Recall Group 9 0.006772 0.00075244 10.918 1.23e-14 Residuals 290 0.019987 0.00006892 - - F1 score Group 9 0.00656 0.00072889 10.654 2.812e-14 Residuals 290 0.01984 0.00006841 - - To discern the differences between groups, a Tukey post hoc test was conducted. This test allows paired comparisons of the means of each group. Since the aim is to determine if using concatenated features yields better results than using EV exclusively. Table 7 presents the results of the Tukey test for the groups that show significant differences between using EV exclusively or the concatenation of EV with k-mers. The complete table can be found on the GitHub page. Table 7. Tukey test results for accuracy comparing EV group and k-mer concatenation groups. The parameters in the table indicate: diff: difference in the means of the compared groups, lwr: lower limit of the confidence interval, upr: upper limit of the confidence interval, p adj: adjusted p-value. Parametro Group Diff lwr Upr p adj Accuracy EV - 3+EV 0.77510000 0.14519745 1.40500256 0.00424300 EV - 5+7+EV -0.72010000 -1.35000255 -0.09019745 0.01159510 EV - 5+EV -0.64796667 -1.27786922 -0.01806411 0.03805910 Precision EV - 3+EV 7.333333E-03 0.00094401 0.01372266 0.01102510 EV - 5+7+EV -6.666667E-03 -0.01305599 -0.00027734 0.03295030 EV - 5+EV -7.333333E-03 -0.01372266 -0.00094401 0.01102510 Loss EV - 5+7+EV 0.95456667 0.08938459 1.81974875 0.01784760 Recall EV - 3+EV 8.333333E-03 0.00149862 0.01516804 0.00485130 F1 score EV - 3+EV 8.333333E-03 0.00152375 0.01514292 0.00459870 In the accuracy parameter, there is a significant difference for the groups ‘3-mers + EV’, ‘5-mers + 7-mers + EV’, and ‘5-mers + EV’. These show ‘p adj’ values lower than α=0.05. The difference between the mean values of the EV group and the ‘3-mers + EV’ group in the ‘diff’ parameter yields a positive value, indicating that the results of the EV group are superior compared to ‘3-mers + EV’. Conversely, the differences of the ‘5-mers + 7-mers + EV’ and ‘5-mers + EV’ groups are negative. This indicates that using these two concatenation groups of k-mers and EV produces better accuracy results than using only EV. For the precision parameter, the mean values of the EV group surpassed those of ‘3-mers + EV’, showing a positive difference. Similarly to accuracy, the exclusive use of EV yields superior precision. However, the groups ‘5-mers + 7-mers + EV’ and ‘5-mers + EV’ exhibited higher mean values than EV, displaying negative differences, indicating that these groups produce better precision than the exclusive use of EV. Regarding the loss parameter, significant differences were observed only between EV and the ‘EV + 5-mers + 7-mers’ group. In contrast to accuracy, the mean values of EV were higher than those of the ‘EV + 5-mers + 7-mers’ group, favoring the concatenated feature group, considering that lower loss percentages are desired in a neural network. Results for the Recall and F1 scores showed significant differences between the EV and ‘3-mers + EV’ groups for both parameters. However, in both cases, the mean values for EV outperformed ‘3-mers + EV’. These results indicate that optimal Recall and F1 scores are generated for the EV group. The Tukey test results indicated that the ‘5-mers + 7-mers + EV’ group produces the best result. The results obtained for each cross-validation fold of this group are shown in Table 8 . Among the cross-validation folds of this group, fold k=22 demonstrated the best result, recording a loss of 8.500%, an accuracy of 91.471%, and a precision, recall, and F1 score of 91.000%. Due to its performance, this methodology was chosen for implementation as the model’s classifier and for incorporating the weights generated in the neural network. Table 8. Performance of our model in comparisons with other methods of machine learning. Method Purpose Database Metrics evaluated Reference Generation of physicochemical characteristics, support vector machine (SVM) and random forest (RF) model. Predict bacteriocin protein sequences 283 bacteriocins and 283 non-bacteriocins Accuracy: 95.54% 67 Word Embedding with Deep Recurrent Neural Networks (RNN) Predict new bacteriocins from protein sequences without using sequence similarity. 346 bacteriocins and 346 non-bacteriocin Accuracy: 99% 72 Sequential Minimal Optimization (SMO)-based classifier Search for relevant characteristics of lantibiotics, which can be used in lantibiotic bioengineering. 280 lantibiotic and 190 non-lantibiotic Accuracy: 88.5% Specificity: 94% 65 Word-embedding algorithm using biophysical properties Design and testing of compounds derived from bacteriocins to generate 20 AA peptides that can be synthesized and their activity evaluated. 346 bacteriocins and 346 non-bacteriocins - 25 Support vector machines (SVM) Identification of biologically active and antimicrobial peptides. 2704 in total Accuracy: 97% 77 Krein-support-vector machine (SVM). Predict the overall antimicrobial activity of sequences Two datasets: 3556 and 3246 1° Datase’s accuracy: 86-92% 2° Dataset’s accuracy: 72-77% 76 Embedding vectors and Deep Learning Neuronal Network (DNN) using k-mers Identification of bacteriocins produced by LAB 24,964 BacLAB and 25,000 Non-BacLAB Accuracy: 91.47% Loss: 8.500% Precision: 91.47 % Recall: 87.66% F1 score: 91% This work Figure 8 illustrates the progress of the loss and accuracy metrics during the 75 epochs of fold 22. The measurements indicated adequate convergence during training. Initially, accuracy revealed low values that progressively increased over epochs, both in training and validation ( Figure 8a ). In contrast, the loss was high during the initial stages of training, decreasing as the training and validation processes progressed ( Figure 8b ). Although attempting to use a larger number of epochs, there was no observed increase in accuracy or decrease in loss beyond the maximum level reached at epoch 70, so this parameter was set at 75, as increasing it would imply greater computational expense without any benefit. Figure 8. Accuracy and Loss evaluation. Visualization of the distributed training metrics for the classifier after 75 epochs from 22° folder, which yielded superior results by employing the concatenation of 5-mers, 7-mers and Embedding Vector. The efficiency of the neural network was assessed using a confusion matrix. The data from the main diagonal were presented, indicating the number of correct predictions made by the model ( Figure 9 ). A total of 732 sequences were correctly classified as non-BacLAB, while 791 were classified as true BacLAB proteins. Values below the main diagonal represent false negatives, where 39 cases were incorrectly classified as non-BacLAB. On the other hand, values above the main diagonal reflect false positives, where 103 cases were incorrectly classified as BacLAB. Figure 9. Confusion matrix of 22° folder. a) The panel shows the confusion matrix for the number of sequences evaluated. b) The panel shows the confusion matrix for the number of sequences evaluated normalized to one. Discussion According to the results of the Tukey test, the concatenation of EV and k-mers did not improve the evaluation metrics for all combinations. When comparing EV with ‘3-mers + EV’, decreases in metrics such as accuracy, precision, and loss were observed for the latter group. This could be caused by using a very short k-mer, which increases the probability of finding these k-mers in non-BacLAB sequences, resulting in more false positives. On the other hand, other combinations like ‘7-mers + EV’, ‘15-mers + EV’, ‘20-mers + EV’, ‘3-mers + 5-mers + EV’, ‘3-mers + 7-mers + EV’, ‘15-mers + 20-mers + EV’ did not show a statistically significant difference for any metric. And it was found that the ‘5-mers + 7-mers + EV’ group produces the best result. The superior performance of the ‘5-mers + 7-mers + EV’ group can be attributed to the selected lengths of k-mers. In several studies, characteristic peptide sequences produced by lactic acid bacteria with lengths of 5 and 7 AA have been identified. Bacteriocins of subclass IIa contain the consensus sequence YGNGVXC at the N-terminal end that characterizes them. Similarly, sequences of leucocin A-UAL 187, sakacin P, and curvacin A had this same 7 AA pattern in their N-terminal region. 9 , 95 However, other articles consider only the highly conserved part, the first 5 AA excluding the variable AA. This characteristic sequence is YGNGV or YGNGL. 13 , 104 , 105 Therefore, given the precedent that certain characteristic sequences of length 5 and 7 exist among bacteriocins, this could explain why the combination of these groups yields better results. On the other hand, the confusion matrix results showed a higher sensitivity rate than specificity. Improving specificity could be considered in future work since, for this study, higher specificity would be preferable over sensitivity. Misclassifying a non-BacLAB as BacLAB could result in losses during laboratory tests if experimental tests are to be implemented. The model developed in this study achieved results within the range reported in the literature. However, it is essential to note that existing tools and models categorize between bacteriocins and non-bacteriocins, unlike the model proposed here, which discriminates between sequences of bacteriocins secreted by LABs or not. For example, the BAGEL software can detect putative gene clusters of bacteriocins in new bacterial genomes and has demonstrated an ROC (Receiver Operating Characteristic) analysis value of 0.99. 106 Comparable to the BLASTP protein search tool, these applications use techniques to help recognize potential bacteriocin sequences by evaluating their similarity to known bacteriocins. 107 Similarly, there is the Bacteriocin Operon and Gene Block Associator (BOA) software , which, unlike other models, identifies homologous gene blocks associated with bacteriocins to predict new ones. 70 The Bacteriocin-Diversity Assessment software (v1.2 version) also performs similar operations. Although these studies mention achieving high accuracy, the specific percentage reached is not mentioned. 108 Additionally, a comparison was made with studies using machine learning and deep learning techniques in Table 8 . In this comparison, as mentioned earlier, the study presents accuracy within the existing literature, surpassing by 3% the work done by Poorinmohammad et al. (2018) 65 and by 4% compared to the results obtained in Redshaw et al. (2023). 78 This work also demonstrated superior performance compared to the BACII𝛼 algorithm, which identifies and classifies bacteriocin sequences. By integrating physicochemical and genomic patterns from known Class II bacteriocin families, it achieved an 86% specificity. 33 Similarly, a better outcome was observed compared to using sequence composition as features. In a study where this feature was used, an accuracy of 90.55% was achieved. 81 Although a similar result was observed compared to the work of Dua et al. (2020), which achieved an accuracy of 91.7%. 109 However, it’s important to consider that each study uses varying amounts of data for their respective articles. Conclusion In this study, a deep learning neural network was developed for the binary classification of bacteriocin amino acid sequences, distinguishing whether they are produced by LAB or not. Feature extraction was performed using the k-mer method and vector embedding. Multiple experiments were conducted with ten different concatenation groups. The results included five lists of 100 characteristic k-mers of LAB-produced bacteriocins for k=3, 5, 7, 15, 20. The concatenation group ‘5-mers + 7-mers + EV’ exhibited better results. With a k-fold cross-validation of k=30, the average results for loss, precision, accuracy, recall, and F1 score were 9.900%, 90.143%, 90.300%, 90.100%, and 90.100%, respectively. Among these, fold 22 demonstrated the best results with a loss of 8.500%, precision of 91.471%, and recall, and F1 score of 91.000%. The model developed in this study achieved consistent results with those seen in the reviewed literature. It outperformed some studies by 3-10%. The lists of characteristic k-mers pave the way to identify new bacteriocins that could be valuable for therapeutic and preventive strategies within the livestock, aquaculture industries, and potentially in human health. This information can also aid in designing and developing more effective and selective synthetic antimicrobial peptides tailored to combat specific pathogens. Ethics and consent Ethical approval and consent were not required. Data availability Underlying data Zenodo: Deep Learning Neural Network Development for the Classification of Bacteriocin Sequences Produced by Lactic Acid Bacteria: Repository. https://doi.org/10.5281/zenodo.13279718 . 103 This project contains the following underlying data: Software-Related Files : • BacLABNet_script.ipynb (Deep Learning Neural Network for classification of Bacteriocin Sequences) • embed_proteins.py (Recurrent Neural Network to obtained the embedding vectors) • model_I22.h5 (This file contains the trained weights of the trained model) • model_I22.json (This file contains the structure of the trained model) • rnn_gru.pt (Initial weights of the Recurrent Neural Network to obtain embedding vectors) • List_kmers.csv (List of 5-mers and 7-mers obtained from dataset after it filtered sequences shorter than 50 aa and longer than 2000 aa) Files Used for Training, Testing, and Validation of the Neural Network • data_nonBacLAB.csv (25000 nonBacLAB amino acid sequences retrieved from Uniprot) • data_BacLAB.csv (24964 BacLAB amino acid sequences retrieved from Uniprot) Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). Extended data Zenodo: Deep Learning Neural Network Development for the Classification of Bacteriocin Sequences Produced by Lactic Acid Bacteria: Repository. https://doi.org/10.5281/zenodo.13279718 . 103 • data_BacLAB_and_nonBacLAB.csv (Combination of sequences from data_BacLAB.csv and data_nonBacLAB.csv) • all k.mers list.xlsx (Table of all k-mers obtained for k=3,5,7,15,20) Data are available under the terms of the ( Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). Software availability Source code available from: https://github.com/lady1004/BacLAB-Deep-Learning-Neural-Network . Archived source code at time of publication: https://doi.org/10.5281/zenodo.13279718 . License: CC0 1.0 Universal . References 1. Yoshida M, Hinkley T, Tsuda S, et al. : Using Evolutionary Algorithms and Machine Learning to Explore Sequence Space for the Discovery of Antimicrobial Peptides. Chem. 2018; 4 (3): 533–543. Publisher Full Text 2. Abiola RR, Okoro EK, Sokunbi O: Lactic Acid Bacteria and the Food Industry - A Comprehensive Review. Int. J. Health Sci. Res. 2022; 12 (5): 128–142. Publisher Full Text 3. Todorov SD, Popov I, Weeks R, et al. : Use of Bacteriocins and Bacteriocinogenic Beneficial Organisms in Food Products: Benefits, Challenges, Concerns. Foods. 2022; 11 . PubMed Abstract | Publisher Full Text | Free Full Text 4. Daba GM, Elnahas MO, Elkhateeb WA: Beyond biopreservatives, bacteriocins biotechnological applications: History, current status, and promising potentials. Biocatal. Agric. Biotechnol. 2022; 39 : 102248. Publisher Full Text 5. Palmer JD, Foster KR: The evolution of spectrum in antibiotics and bacteriocins. Proc. Natl. Acad. Sci. USA. 2022; 119 (38): e2205407119. PubMed Abstract | Publisher Full Text | Free Full Text 6. Arthur TD, Cavera VL, Chikindas ML: On bacteriocin delivery systems and potential applications. Future Microbiol. 2014; 9 : 235–248. PubMed Abstract | Publisher Full Text 7. Ding D, Wang B, Zhang X, et al. : The spread of antibiotic resistance to humans and potential protection strategies. Ecotoxicol. Environ. Saf. 2023; 254 : 114734. PubMed Abstract | Publisher Full Text 8. Timothy B, Iliyasu AH, Anvikar AR: Bacteriocins of Lactic Acid Bacteria and Their Industrial Application. Current Topic in Lactic Acid Bacteria and Probiotics. 2021; 7 (1): 1–13. Publisher Full Text 9. Negash AW, Tsehai BA: Current Applications of Bacteriocin. Int. J. Microbiol. 2020; 2020 : 1–7. PubMed Abstract | Publisher Full Text | Free Full Text 10. Gradisteanu Pircalabioru G, Popa LI, Marutescu L, et al. : Bacteriocins in the era of antibiotic resistance: rising to the challenge. Pharmaceutics. 2021; 13 . PubMed Abstract | Publisher Full Text | Free Full Text 11. Soltani S, Hammami R, Cotter PD, et al. : Bacteriocins as a new generation of antimicrobials: Toxicity aspects and regulations. FEMS Microbiol. Rev. 2021; 45 . PubMed Abstract | Publisher Full Text | Free Full Text 12. Silva CCG, Silva SPM, Ribeiro SC: Application of bacteriocins and protective cultures in dairy food preservation. Front. Microbiol. 2018; 9 . PubMed Abstract | Publisher Full Text | Free Full Text 13. Hernández-González JC, Martínez-Tapia A, Lazcano-Hernández G, et al. : Bacteriocins from lactic acid bacteria. A powerful alternative as antimicrobials, probiotics, and immunomodulators in veterinary medicine. Animals. 2021; 11 . PubMed Abstract | Publisher Full Text | Free Full Text 14. Parada JL, Caron CR, Medeiros ABP, et al. : Bacteriocins from lactic acid bacteria: Purification, properties and use as biopreservatives. Braz. Arch. Biol. Technol. 2007; 50 (3): 512–542. Publisher Full Text 15. Abdulhussain Kareem R, Razavi SH: Plantaricin bacteriocins: As safe alternative antimicrobial peptides in food preservation—A review. J. Food Saf. 2020; 40 (1). Publisher Full Text 16. Alvarez-Sieiro P, Montalbán-López M, Mu D, et al. : Bacteriocins of lactic acid bacteria: extending the family. Appl. Microbiol. Biotechnol. 2016; 100 : 2939–2951. PubMed Abstract | Publisher Full Text | Free Full Text 17. Simons A, Alhanout K, Duval RE: Bacteriocins, antimicrobial peptides from bacterial origin: Overview of their biology and their impact against multidrug-resistant bacteria. Microorganisms. 2020; 8 . PubMed Abstract | Publisher Full Text | Free Full Text 18. Darbandi A, Asadi A, Mahdizade Ari M, et al. : Bacteriocins: Properties and potential use as antimicrobials. J. Clin. Lab. Anal. 2022; 36 . Publisher Full Text 19. Ibrahim OO: Classification of Antimicrobial Peptides Bacteriocins, and the Nature of Some Bacteriocins with Potential Applications in Food Safety and Bio-Pharmaceuticals. EC Microbiol. 2019; 15 (7): 591–608. 20. Verma DK, Thakur M, Singh S, et al. : Bacteriocins as antimicrobial and preservative agents in food: Biosynthesis, separation and application. Food Biosci. 2022; 46 : 101594. Publisher Full Text 21. Lee YCJ, Cowan A, Tankard A: Peptide Toxins as Biothreats and the Potential for AI Systems to Enhance Biosecurity. Front. Bioeng. Biotechnol. 2022; 10 . Publisher Full Text 22. Wang Y, Wu J, Lv M, et al. : Metabolism Characteristics of Lactic Acid Bacteria and the Expanding Applications in Food Industry. Front. Bioeng. Biotechnol. 2021; 9 . Publisher Full Text 23. Xu C, Fu Y, Liu F, et al. : Purification and antimicrobial mechanism of a novel bacteriocin produced by Lactobacillus rhamnosus 1.0320. LWT. 2021 Jun; 137 : 110338. Publisher Full Text 24. Cardona AF, Ruíz Patiño A, Jaller E, et al. : Caminando a hombros de gigantes: intersección entre la genómica y la IA. Medicina (B Aires). 2022; 43 (4): 668–681. Publisher Full Text 25. Fields FR, Freed SD, Carothers KE, et al. : Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains. Drug Dev. Res. 2020; 81 (1): 43–51. PubMed Abstract | Publisher Full Text | Free Full Text 26. Akhter S, Miller JH: BaPreS: a software tool for predicting bacteriocins using an optimal set of features. BMC Bioinformatics. 2023; 24 (1): 313. PubMed Abstract | Publisher Full Text | Free Full Text 27. Talat A, Khan AU: Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections. Drug Discov. Today. 2023; 28 : 103491. PubMed Abstract | Publisher Full Text 28. Xu Y, Verma D, Sheridan RP, et al. : Deep Dive into Machine Learning Models for Protein Engineering. J. Chem. Inf. Model. 2020; 60 (6): 2773–2790. PubMed Abstract | Publisher Full Text 29. Ng ZJ, Zarin MA, Lee CK, et al. : Application of bacteriocins in food preservation and infectious disease treatment for humans and livestock: A review. RSC Adv. 2020; 10 : 38937–38964. PubMed Abstract | Publisher Full Text | Free Full Text 30. Yassin MT, Abdel-Fattah Mostafa A, Al-Askar AA, et al. : In vitro antimicrobial potency of Elettaria cardamomum ethanolic extract against multidrug resistant of food poisoning bacterial strains. J. King Saud. Univ. Sci. 2022; 34 (6): 102167. Publisher Full Text 31. Sun MC, Hu ZY, Li DD, et al. : Application of the Reuterin System as Food Preservative or Health-Promoting Agent: A Critical Review. Foods. 2022; 11 . PubMed Abstract | Publisher Full Text | Free Full Text 32. Gupta R, Kumar R: Impact Of Chemical Food Preservatives On Human Health. PalArch's J. Archaeol. Egypt/Egyptol. 2021; 15 (15). 33. Ye P, Wang J, Liu M, et al. : Purification and characterization of a novel bacteriocin from Lactobacillus paracasei ZFM54. LWT. 2021; 143 : 111125. Publisher Full Text 34. Yu HH, Chin YW, Paik HD: Application of natural preservatives for meat and meat products against food-borne pathogens and spoilage bacteria: A review. Foods. 2021; 10 . PubMed Abstract | Publisher Full Text | Free Full Text 35. Ortega-Morales BO, Gaylarde CC: Bioconservation of historic stone buildings—an updated review. Appl. Sci. (Switzerland). 2021; 11 . Publisher Full Text 36. Pato U, Riftyan E, Ayu DF, et al. : Antibacterial efficacy of lactic acid bacteria and bacteriocin isolated from Dadih’s against Staphylococcus aureus . Food Sci. Technol (Brazil). 2022; 42 : 42. Publisher Full Text 37. Yap PG, Lai ZW, Tan JS: Bacteriocins from lactic acid bacteria: purification strategies and applications in food and medical industries: a review. Beni-Suef University Journal of Basic and Applied Sciences. 2022; 11 . Publisher Full Text 38. Zimina M, Babich O, Prosekov A, et al. : Overview of global trends in classification, methods of preparation and application of bacteriocins. Antibiotics. 2020; 9 (9). PubMed Abstract | Publisher Full Text | Free Full Text 39. Xihui Z, Yanlan L, Zhiwei W, et al. : Antibiotic resistance of Riemerella anatipestifer and comparative analysis of antibiotic-resistance gene detection methods. Poult. Sci. 2023; 102 (3): 102405. PubMed Abstract | Publisher Full Text | Free Full Text 40. Parmanik A, Das S, Kar B, et al. : Current Treatment Strategies Against Multidrug-Resistant Bacteria: A Review. Curr. Microbiol. 2022; 79 : 388. PubMed Abstract | Publisher Full Text | Free Full Text 41. El IK, Senhaji NS, Zinebi S, et al. : Potential application of bacteriocin produced from lactic acid bacteria. Microbiol. Biotechnol. Lett. 2020; 48 : 237–251. Publisher Full Text 42. Klibi N, Ben Slimen N, Fhoula I, et al. : Genotypic diversity, antibiotic resistance and bacteriocin production of enterococci isolated from rhizospheres. Microbes Environ. 2012; 27 (4): 533–537. PubMed Abstract | Publisher Full Text | Free Full Text 43. Lehtinen S, Croucher NJ, Blanquart F, et al. : Epidemiological dynamics of bacteriocin competition and antibiotic resistance. Proc. R. Soc. B Biol. Sci. 2022; 289 (1984). Publisher Full Text 44. Guryanova SV: Immunomodulation, Bioavailability and Safety of Bacteriocins. Life. 2023; 13 . PubMed Abstract | Publisher Full Text | Free Full Text 45. Ahmad V, Khan MS, Jamal QMS, et al. : Antimicrobial potential of bacteriocins: in therapy, agriculture and food preservation. Int. J. Antimicrob. Agents. 2017; 49 : 1–11. PubMed Abstract | Publisher Full Text 46. Demment MW, Young MM, Sensenig RL: Animal Source Foods to Improve Micronutrient Nutrition and Human Function in Developing Countries Providing Micronutrients through Food-Based Solutions: A Key to Human and National Development. J. Nutr. 2003; 133 : 3879S–3885S. Publisher Full Text 47. Scialabba NEH: Livestock food and human nutrition. Managing Healthy Livestock Production and Consumption; 2021. 48. Varijakshapanicker P, McKune S, Miller L, et al. : Sustainable livestock systems to improve human health, nutrition, and economic status. Anim. Front. 2019; 9 (4): 39–50. PubMed Abstract | Publisher Full Text | Free Full Text 49. Pieterse R, Todorov SD, Dicks LMT: Mode of action and in vitro susceptibility of mastitis pathogens to macedocin ST91KM and preparation of a teat seal containing the bacteriocin. Braz. J. Microbiol. 2010; 41 (1): 133–145. PubMed Abstract | Publisher Full Text | Free Full Text 50. Pieterse R, Todorov SD: Bacteriocins: Exploring alternatives to antibiotics in mastitis treatment. Braz. J. Microbiol. 2010; 41 : 542–562. PubMed Abstract | Publisher Full Text | Free Full Text 51. Sanca FMM, Blanco IR, Dias M, et al. : Antimicrobial Activity of Peptides Produced by Lactococcus lactis subsp. lactis on Swine Pathogens. Animals. 2023; 13 (15). PubMed Abstract | Publisher Full Text | Free Full Text 52. Bemena LD, Mohamed LA, Fernandes AM, et al. : Applications of bacteriocins in food, livestock health and medicine. Int. J. Curr. Microbiol. App. Sci. 2014; 3 (12). 53. Callaway TR, Anderson RC, Edrington TS, et al. : Recent pre-harvest supplementation strategies to reduce carriage and shedding of zoonotic enteric bacterial pathogens in food animals. Anim. Health Res. Rev. 2004; 5 (1): 35–47. PubMed Abstract | Publisher Full Text 54. Rodríguez E, Arqués JL, Rodríguez R, et al. : Reuterin production by lactobacilli isolated from pig faeces and evaluation of probiotic traits. Lett. Appl. Microbiol. 2003; 37 (3): 259–263. PubMed Abstract | Publisher Full Text 55. Khoramian B, Emaneini M, Bolourchi M, et al. : Therapeutic effects of a combined antibiotic-enzyme treatment on subclinical mastitis in lactating dairy cows. Vet. Med (Praha). 2016; 61 (5): 237–242. Publisher Full Text 56. Zadoks RN, Middleton JR, McDougall S, et al. : Molecular epidemiology of mastitis pathogens of dairy cattle and comparative relevance to humans. J. Mammary Gland Biol. Neoplasia. 2011; 16 (4): 357–372. PubMed Abstract | Publisher Full Text | Free Full Text 57. Hai NV: The use of probiotics in aquaculture. J. Appl. Microbiol. 2015; 119 : 917–935. Publisher Full Text 58. Corripio-Miyar Y, Mazorra de Quero C, Treasurer JW, et al. : Vaccination experiments in the gadoid haddock, Melanogrammus aeglefinus L., against the bacterial pathogen Vibrio anguillarum. Vet. Immunol. Immunopathol. 2007; 118 (1–2): 147–153. PubMed Abstract | Publisher Full Text 59. Smith P: Antimicrobial use in shrimp farming in Ecuador and emerging multi-resistance during the cholera epidemic of 1991: A re-examination of the data. Aquaculture. 2007; 271 : 1–7. Publisher Full Text 60. Zhou X, Wang Y: Probiotics in Aquaculture - Benefits to the Health, Technological Applications and Safety. Health and Environment in Aquaculture. 2012. 61. Nathanailides C, Kolygas M, Choremi K, et al. : Probiotics have the potential to significantly mitigate the environmental impact of freshwater fish farms. Fishes. 2021; 6 . Publisher Full Text 62. Wang YB: Effect of probiotics on growth performance and digestive enzyme activity of the shrimp Penaeus vannamei. Aquaculture. 2007; 269 (1–4): 259–264. Publisher Full Text 63. Amenyogbe E: Application of probiotics for sustainable and environment-friendly aquaculture management - A review. Cogent Food Agric. 2023; 9 . Publisher Full Text 64. Taoka Y, Maeda H, Jo JY, et al. : Growth, stress tolerance and non-specific immune response of Japanese flounder Paralichthys olivaceus to probiotics in a closed recirculating system. Fish. Sci. 2006; 72 (2): 310–321. Publisher Full Text 65. Poorinmohammad N, Hamedi J: Moghaddam MHAM. Sequence-based analysis and prediction of lantibiotics: A machine learning approach. Comput. Biol. Chem. 2018; 77 : 199–206. PubMed Abstract | Publisher Full Text 66. Yount NY, Weaver DC, de Anda J , et al. : Discovery of Novel Type II Bacteriocins Using a New High-Dimensional Bioinformatic Algorithm. Front. Immunol. 2020; 11 . PubMed Abstract | Publisher Full Text | Free Full Text 67. Akhter S, Miller J: Optimal feature selection and software tool development for bacteriocin prediction. bioRxiv. 2022. 68. van Heel AJ , de Jong A , Montalbán-López M, et al. : BAGEL3: Automated identification of genes encoding bacteriocins and (non-)bactericidal posttranslationally modified peptides. Nucleic Acids Res. 2013; 41 (Web Server issue): W448–W453. Publisher Full Text 69. Hammami R, Zouhir A, Le Lay C, et al. : BACTIBASE second release: A database and tool platform for bacteriocin characterization. BMC Microbiol. 2010; 10 . PubMed Abstract | Publisher Full Text | Free Full Text 70. Morton JT, Freed SD, Lee SW, et al. : A large scale prediction of bacteriocin gene blocks suggests a wide functional spectrum for bacteriocins. BMC Bioinformatics. 2015; 16 (1): 381. PubMed Abstract | Publisher Full Text | Free Full Text 71. Nguyen TTD, Le NQK, Ho QT, et al. : Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters. Anal. Biochem. 2019; 577 : 73–81. PubMed Abstract | Publisher Full Text 72. Hamid MN, Friedberg I: Identifying antimicrobial peptides using word embedding with deep recurrent neural networks. Bioinformatics. 2019; 35 (12): 2009–2016. PubMed Abstract | Publisher Full Text | Free Full Text 73. Li C, Sutherland D, Hammond SA, et al. : AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genomics. 2022; 23 (1). Publisher Full Text 74. Wang Y, Wang L, Li C, et al. : AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides. Front. Genet. 2023; 14 : 14. Publisher Full Text 75. Lee H, Lee S, Lee I, et al. : AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model. Protein Sci. 2023; 32 (1): e4529. PubMed Abstract | Publisher Full Text | Free Full Text 76. Ruiz Puentes P, Henao MC, Cifuentes J, et al. : Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence. Membranes (Basel). 2022; 12 (7). Publisher Full Text 77. Gull S, Shamim N, Minhas F: AMAP: Hierarchical multi-label prediction of biologically active and antimicrobial peptides. Comput. Biol. Med. 2019; 107 : 172–181. PubMed Abstract | Publisher Full Text 78. Redshaw J, Ting DSJ, Brown A, et al. : Krein support vector machine classification of antimicrobial peptides. Dig. Dis. 2023; 2 (2): 502–511. Publisher Full Text 79. Porto WF, Ferreira KCV, Ribeiro SM, et al. : Sense the moment: A highly sensitive antimicrobial activity predictor based on hydrophobic moment. Biochim. Biophys. Acta Gen. Subj. 2022; 1866 (3): 130070. PubMed Abstract | Publisher Full Text 80. Yan J, Bhadra P, Li A, et al. : Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning. Mol. Ther. Nucleic Acids. 2020; 20 : 882–894. PubMed Abstract | Publisher Full Text | Free Full Text 81. Veltri D, Kamath U, Shehu A: Deep learning improves antimicrobial peptide recognition. Bioinformatics. 2018; 34 (16): 2740–2747. PubMed Abstract | Publisher Full Text | Free Full Text 82. Bateman A, Martin MJ, Orchard S, et al. : UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023; 51 (D1). 83. Mokoena MP: Lactic acid bacteria and their bacteriocins: Classification, biosynthesis and applications against uropathogens: A mini-review. Molecules. 2017; 22 . PubMed Abstract | Publisher Full Text | Free Full Text 84. Jain C, Rhie A, Zhang H, et al. : Weighted minimizer sampling improves long read mapping. Bioinformatics. 2020; 36 : i111–i118. PubMed Abstract | Publisher Full Text | Free Full Text 85. Edgar R: Syncmers are more sensitive than minimizers for selecting conserved k-mers in biological sequences. PeerJ. 2021; 9 : e10805. Publisher Full Text 86. Wang Y, Chen Q, Deng C, et al. : KmerGO: A Tool to Identify Group-Specific Sequences With k-mers. Front. Microbiol. 2020; 11 . Publisher Full Text 87. Shadab S, Alam Khan MT, Neezi NA, et al. : DeepDBP: Deep neural networks for identification of DNA-binding proteins. Inform. Med. Unlocked. 2020; 19 : 100318. Publisher Full Text 88. Basiri ME, Nemati S, Abdar M, et al. : ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis. Futur. Gener. Comput. Syst. 2021; 115 : 279–294. Publisher Full Text 89. Onan A: Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. J. King Saud Univ. Comput. Inf. Sci. 2022; 34 (5): 2098–2117. Publisher Full Text 90. Shen Z, Bao W, Huang DS: Recurrent Neural Network for Predicting Transcription Factor Binding Sites. Sci. Rep. 2018; 8 (1): 15270. PubMed Abstract | Publisher Full Text | Free Full Text 91. Hu S, Ma R, Wang H: An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences. PLoS One. 2019; 14 (11): e0225317. PubMed Abstract | Publisher Full Text | Free Full Text 92. Angelopoulou A, Warda AK, O’Connor PM, et al. : Diverse Bacteriocins Produced by Strains From the Human Milk Microbiota. Front. Microbiol. 2020; 11 : 11. Publisher Full Text 93. Chen H, Yan X, Tian F, et al. : Cloning, expression, and identification of a novel class IIa bacteriocin in the Escherichia coli cell-free protein expression system. Biotechnol. Lett. 2012; 34 (2): 359–364. PubMed Abstract | Publisher Full Text 94. Martinez JM, Kok J, Sanders JW, et al. : Heterologous coproduction of enterocin A and pediocin PA-1 by Lactococcus lactis: Detection by specific peptide-directed antibodies. Appl. Environ. Microbiol. 2000; 66 (8): 3543–3549. PubMed Abstract | Publisher Full Text | Free Full Text 95. Lozano JCN, Meyer JN, Sletten K, et al. : Purification and amino acid sequence of a bacteriocin produced by Pediococcus acidilactici. J. Gen. Microbiol. 1992; 138 (9): 1985–1990. PubMed Abstract | Publisher Full Text 96. Kashyap DR: Microbial metabolites: Peptides of diverse structure and function. New and Future Developments in Microbial Biotechnology and Bioengineering: Microbial Secondary Metabolites Biochemistry and Applications. 2019. Publisher Full Text 97. Villalba-Meneses F, Gudiño Gomezjurado ME, Suquilanda-Pesántez JD, et al. : NIFtHool: An informatics program for identification of NifH proteins using deep neural networks. F1000Res. 2022; 11 : 11. Publisher Full Text 98. Zhang J, Zong C: Deep Neural Networks in Machine Translation: An Overview. IEEE Intell. Syst. 2015; 30 : 16–25. Publisher Full Text 99. McKinney W: Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference. 2010. 100. Pedregosa F, Varoquaux G, Gramfort A, et al. : Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 2011; 12 . 101. Harris CR, Millman KJ, van der Walt SJ , et al. : Array programming with NumPy. Nature. 2020; 585 : 357–362. PubMed Abstract | Publisher Full Text | Free Full Text 102. Cichy RM, Kaiser D: Deep Neural Networks as Scientific Models. Trends Cogn. Sci. 2019; 23 : 305–317. Publisher Full Text 103. González LL: Deep Learning Neural Network Development for the Classification of Bacteriocin Sequences Produced by Lactic Acid Bacteria. Zenodo. 2024. Publisher Full Text 104. Liu W, Zhang L, Yi H, et al. : Qualitative detection of class IIa bacteriocinogenic lactic acid bacteria from traditional Chinese fermented food using a YGNGV-motif-based assay. J. Microbiol. Methods. 2014; 100 (1): 121–127. PubMed Abstract | Publisher Full Text 105. Sood SK, Vijay Simha B, Kumariya R, et al. : Highly Specific Culture-Independent Detection of YGNGV Motif-Containing Pediocin-Producing Strains. Probiotics Antimicrob. Proteins. 2013; 5 (1): 37–42. PubMed Abstract | Publisher Full Text 106. Chiou PT, Alotaibi AS, Halfond WGJ: BAGEL: An Approach to Automatically Detect Navigation-Based Web Accessibility Barriers for Keyboard Users. Conference on Human Factors in Computing Systems - Proceedings. 2023. 107. Boratyn GM, Camacho C, Cooper PS, et al. : BLAST: a more efficient report with usability improvements. Nucleic Acids Res. 2013; 41 (Web Server issue): W29–W33. PubMed Abstract | Publisher Full Text | Free Full Text 108. Costa SS, da Silva Moia G , Silva A, et al. : BADASS: BActeriocin-Diversity ASsessment Software. BMC Bioinformatics. 2023; 24 (1): 24. PubMed Abstract | Publisher Full Text | Free Full Text 109. Dua M, Barbará D, Shehu A: Exploring deep neural network architectures: A case study on improving antimicrobial peptide recognition. EPiC Series in Computing. 2020. Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 30 Aug 2024 ADD YOUR COMMENT Comment Author details Author details 1 School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, Provincia de Imbabura, 100119, Ecuador Lady L. González Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Isaac Arias-Serrano Roles: Supervision, Validation, Visualization, Writing – Review & Editing Fernando Villalba-Meneses Roles: Resources, Software, Validation, Visualization Paulo Navas-Boada Roles: Validation, Visualization, Writing – Review & Editing Jonathan Cruz-Varela Roles: Conceptualization, Methodology, Project Administration, Software, Supervision, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 20 Jun 2025, 13:981 https://doi.org/10.12688/f1000research.154432.2 version 1 Published: 30 Aug 2024, 13:981 https://doi.org/10.12688/f1000research.154432.1 Copyright © 2024 González LL et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article González LL, Arias-Serrano I, Villalba-Meneses F et al. Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.12688/f1000research.154432.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 30 Aug 2024 Views 0 Cite How to cite this report: Niamah AK. Reviewer Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.169463.r355998 ) The direct URL for this report is: https://f1000research.com/articles/13-981/v1#referee-response-355998 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 27 Jan 2025 Alaa Kareem Niamah , University of Basrah, Basrah, Basra Governorate, Iraq Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.169463.r355998 Dear Editors and Authors 1-The introduction needs to be supported by some sources related to the current study, such as: refer 1 and 2 ‏ 2-The aim of the study is not clear and is ... Continue reading READ ALL Dear Editors and Authors 1-The introduction needs to be supported by some sources related to the current study, such as: refer 1 and 2 ‏ 2-The aim of the study is not clear and is not mentioned in the manuscript introduction. 3-The labeling of the x-axis in Figure 2 should be corrected. 4-Table 2 has no meaning and should be deleted. Since the coding is known and previously included in references. 5-Figure 6 is not clear. It should be explained better than the current situation and explain what these shapes, such as circles and arrows, mean. 6-Table 8 should preferably be written and discussed with the results of previous references, not as in the current situation. It is not permissible to put a table containing the results of others. 7-Figure 8 The authors did not explain what A and B mean. 8-Figure 8 The authors did not explain what a and b mean. 9-The conclusions are very poor. This chapter is dedicated to the conclusions of the current study, but we see that the authors have mentioned many results. This chapter should be rewritten and all the results mentioned should be deleted. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Niamah A: Structure, mode of action and application of pediocin natural antimicrobial food preservative: A review. Basrah Journal of Agricultural Sciences . 2018; 31 (1): 59-69 Publisher Full Text 2. Niamah AK, Al-Sahlany STG, Verma DK, Shukla RM, et al.: Emerging lactic acid bacteria bacteriocins as anti-cancer and anti-tumor agents for human health. Heliyon . 2024; 10 (17): e37054 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bacteriocin production , Lactic acid Bacteria I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Niamah AK. Reviewer Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.169463.r355998 ) The direct URL for this report is: https://f1000research.com/articles/13-981/v1#referee-response-355998 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 11 Sep 2025 Lady González , School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, 100119, Ecuador 11 Sep 2025 Author Response We thank you for your constructive comments. We have addressed all points as follows: The requested sources have been added to the introduction. The study's aim has ... Continue reading We thank you for your constructive comments. We have addressed all points as follows: The requested sources have been added to the introduction. The study's aim has been clarified in the introduction. We added explicit statement in the introduction's final paragraph. The caption for Figure 2 has been corrected. A more appropriate description was also given to the figure. Table 2 has been removed. A clearer explanation of Figure 6 has been added. Table 8 was misplaced; it has now been moved to the discussion section. Added an explanation of what (a) and (b) represent in Figure 8. Added an explanation of what (a) and (b) represent in Figure 8. The conclusion has been rewritten. Aspects such as practical limitations (dataset bias), and future directions (experimental validation) were added in the conclusión. We thank you for your constructive comments. We have addressed all points as follows: The requested sources have been added to the introduction. The study's aim has been clarified in the introduction. We added explicit statement in the introduction's final paragraph. The caption for Figure 2 has been corrected. A more appropriate description was also given to the figure. Table 2 has been removed. A clearer explanation of Figure 6 has been added. Table 8 was misplaced; it has now been moved to the discussion section. Added an explanation of what (a) and (b) represent in Figure 8. Added an explanation of what (a) and (b) represent in Figure 8. The conclusion has been rewritten. Aspects such as practical limitations (dataset bias), and future directions (experimental validation) were added in the conclusión. Competing Interests: The authors declare that they have no competing interests. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 11 Sep 2025 Lady González , School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, 100119, Ecuador 11 Sep 2025 Author Response We thank you for your constructive comments. We have addressed all points as follows: The requested sources have been added to the introduction. The study's aim has ... Continue reading We thank you for your constructive comments. We have addressed all points as follows: The requested sources have been added to the introduction. The study's aim has been clarified in the introduction. We added explicit statement in the introduction's final paragraph. The caption for Figure 2 has been corrected. A more appropriate description was also given to the figure. Table 2 has been removed. A clearer explanation of Figure 6 has been added. Table 8 was misplaced; it has now been moved to the discussion section. Added an explanation of what (a) and (b) represent in Figure 8. Added an explanation of what (a) and (b) represent in Figure 8. The conclusion has been rewritten. Aspects such as practical limitations (dataset bias), and future directions (experimental validation) were added in the conclusión. We thank you for your constructive comments. We have addressed all points as follows: The requested sources have been added to the introduction. The study's aim has been clarified in the introduction. We added explicit statement in the introduction's final paragraph. The caption for Figure 2 has been corrected. A more appropriate description was also given to the figure. Table 2 has been removed. A clearer explanation of Figure 6 has been added. Table 8 was misplaced; it has now been moved to the discussion section. Added an explanation of what (a) and (b) represent in Figure 8. Added an explanation of what (a) and (b) represent in Figure 8. The conclusion has been rewritten. Aspects such as practical limitations (dataset bias), and future directions (experimental validation) were added in the conclusión. Competing Interests: The authors declare that they have no competing interests. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Georrge JJ. Reviewer Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.169463.r355994 ) The direct URL for this report is: https://f1000research.com/articles/13-981/v1#referee-response-355994 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 11 Jan 2025 John J. Georrge , University of North Bengal, Darjeeling, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.169463.r355994 Abstract The research investigates a deep learning neural network for the binary classification of bacteriocin sequences produced by lactic acid bacteria (LAB). Utilizing k-mers and vector embeddings for feature extraction, the study tested ten group combinations, concluding ... Continue reading READ ALL Abstract The research investigates a deep learning neural network for the binary classification of bacteriocin sequences produced by lactic acid bacteria (LAB). Utilizing k-mers and vector embeddings for feature extraction, the study tested ten group combinations, concluding that the concatenated features of 5-mers, 7-mers, and embedding vectors (EV) yielded superior results. With k-fold cross-validation (k=30), the model achieved notable accuracy (91.47%), precision, recall, and F1 score (91%). These results demonstrate a promising approach for identifying bacteriocins, paving the way for applications in medicine, livestock, aquaculture, and food preservation. Weaknesses: The abstract lacks explicit mention of the practical challenges or limitations, such as computational expense or data imbalance, which are critical for assessing the feasibility of real-world applications. Introduction The study highlights the growing challenge of antibiotic resistance and the potential of bacteriocins as alternatives. LAB bacteriocins, recognized as GRAS (Generally Recognized as Safe) and QPS (Qualified Presumption of Safety), show promise for therapeutic and industrial applications. The introduction emphasizes the critical need for efficient classification methods, leveraging artificial intelligence (AI) and deep learning to address the limitations of traditional genomic tools. Existing methods like BAGEL and BLASTP rely heavily on sequence homology, often leading to false negatives. The study proposes a deep neural network as an innovative solution for identifying LAB-produced bacteriocins. Weaknesses: While the introduction effectively frames the problem, it does not provide sufficient detail on the limitations of existing deep learning models, nor does it address the potential biases introduced by selecting specific LAB genera. Methods The research employed amino acid (AA) sequences sourced from the UniProt database, filtered for lengths between 50 and 2000 AAs. LAB bacteriocin sequences were labelled as “BacLAB,” and non-LAB sequences as “Non-BacLAB.” Feature extraction was performed using k-mers of varying lengths (3, 5, 7, 15, and 20) and embedding vectors generated via a Gated Recurrent Unit (GRU)-based recurrent neural network (RNN). The concatenated features were inputs to a deep neural network (DNN) structured in four blocks with 13 layers. The model used Adam optimization, a mean absolute error loss function, and k-fold cross-validation (k=30). Statistical analyses included ANOVA and Tukey tests to validate performance metrics. Weaknesses: The methods section lacks clarity on how hyperparameters were tuned and does not justify the selection of specific k-mer lengths. Results The study identified five lists of 100 characteristic k-mers for each selected length. Performance metrics from cross-validation demonstrated that the concatenation of 5-mers, 7-mers, and embedding vectors (5-mers+7-mers+EV) achieved the best results with: Loss: 8.50% Accuracy: 91.47% Precision, Recall, F1 Score: 91.00% Significant differences in accuracy and loss were observed between groups. The confusion matrix revealed high sensitivity but lower specificity, highlighting potential areas for improvement. Compared to other machine learning models, this study’s approach exceeded accuracy benchmarks by 3-10%. Weaknesses: While the results are promising, the limited scope of comparison with other algorithms leaves room for a more robust benchmarking process. Discussion The superior performance of the 5-mers+7-mers+EV group is attributed to characteristic motifs in bacteriocins. The study’s findings align with known sequences in subclass IIa bacteriocins, such as YGNGVXC. The results underscore the importance of selecting appropriate k-mer lengths to balance specificity and sensitivity. However, limitations include the model’s higher false positive rate for non-BacLAB sequences, indicating room for refinement in feature selection. Additionally, comparisons with existing tools like BAGEL reveal the unique advantage of the proposed approach in distinguishing LAB-specific bacteriocins. Weaknesses: The discussion does not critically evaluate the computational cost and scalability of the model for larger datasets. It also overlooks the potential need for experimental validation of the predictions to confirm biological relevance. Conclusion The study successfully developed a deep learning-based classification model for LAB bacteriocins, achieving consistent results comparable to and sometimes exceeding existing methods. The identified k-mers and embeddings offer a robust foundation for future work in therapeutic, aquacultural, and industrial applications. While promising, further specificity and practical testing improvements are essential to validate and expand the model’s utility. Weaknesses: The conclusion is overly optimistic and does not sufficiently address the study’s limitations, such as the reliance on publicly available datasets that may not represent all bacteriocin-producing LAB. References References were cited appropriately within the research, highlighting the breadth of prior work on bacteriocins and machine learning applications. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Rational Drug Designing, Novel Drug Target Identification, Cheminformatics, Bioinformatics, Molecular Modelling, Docking, QSAR, Pharmacohpore, Protein Engineering, Bioactive Peptides, Reverse Vaccinology, Artificial Intelligence, and Machine Learning. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Georrge JJ. Reviewer Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.169463.r355994 ) The direct URL for this report is: https://f1000research.com/articles/13-981/v1#referee-response-355994 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 11 Sep 2025 Lady González , School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, 100119, Ecuador 11 Sep 2025 Author Response We sincerely appreciate your constructive feedback. We have addressed all raised concerns through the following revisions: Abstract : Added limitations regarding computational costs and class imbalance to assess real-world feasibility. Introduction : ... Continue reading We sincerely appreciate your constructive feedback. We have addressed all raised concerns through the following revisions: Abstract : Added limitations regarding computational costs and class imbalance to assess real-world feasibility. Introduction : Expanded on limitations of existing deep learning models. Discussed potential biases from selecting specific LAB genera (e.g., underrepresentation of rare bacteriocin producers). Methods : Clarified hyperparameter tuning. Justified k-mer lengths based on conserved bacteriocin motifs (e.g., "pediocin box" for *k*=14–19) and cited prior studies. Added scalability details: Experiments were conducted on Google Colab. Results: A comparison was made with alternative models. However, it was developed in more depth in the discussion section. Discussion : While our model demonstrates robust performance on the current dataset, its scalability to larger datasets requires further empirical validation. Although computational costs are expected to increase linearly with sequence length due to our fixed-dimension k-mer/embedding pipeline, real-world performance may vary with dataset diversity. Furthermore, while we recognize the importance of experimental validation through biological testing, this remains an area for future research as it falls beyond the scope of our current computational study Conclusions : We have moderated our claims by emphasizing key limitations, including dependence on public datasets (which may introduce gaps in LAB diversity representation) and the need for experimental validation in future work. We sincerely appreciate your constructive feedback. We have addressed all raised concerns through the following revisions: Abstract : Added limitations regarding computational costs and class imbalance to assess real-world feasibility. Introduction : Expanded on limitations of existing deep learning models. Discussed potential biases from selecting specific LAB genera (e.g., underrepresentation of rare bacteriocin producers). Methods : Clarified hyperparameter tuning. Justified k-mer lengths based on conserved bacteriocin motifs (e.g., "pediocin box" for *k*=14–19) and cited prior studies. Added scalability details: Experiments were conducted on Google Colab. Results: A comparison was made with alternative models. However, it was developed in more depth in the discussion section. Discussion : While our model demonstrates robust performance on the current dataset, its scalability to larger datasets requires further empirical validation. Although computational costs are expected to increase linearly with sequence length due to our fixed-dimension k-mer/embedding pipeline, real-world performance may vary with dataset diversity. Furthermore, while we recognize the importance of experimental validation through biological testing, this remains an area for future research as it falls beyond the scope of our current computational study Conclusions : We have moderated our claims by emphasizing key limitations, including dependence on public datasets (which may introduce gaps in LAB diversity representation) and the need for experimental validation in future work. Competing Interests: The authors declare that they have no competing interests. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 11 Sep 2025 Lady González , School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, 100119, Ecuador 11 Sep 2025 Author Response We sincerely appreciate your constructive feedback. We have addressed all raised concerns through the following revisions: Abstract : Added limitations regarding computational costs and class imbalance to assess real-world feasibility. Introduction : ... Continue reading We sincerely appreciate your constructive feedback. We have addressed all raised concerns through the following revisions: Abstract : Added limitations regarding computational costs and class imbalance to assess real-world feasibility. Introduction : Expanded on limitations of existing deep learning models. Discussed potential biases from selecting specific LAB genera (e.g., underrepresentation of rare bacteriocin producers). Methods : Clarified hyperparameter tuning. Justified k-mer lengths based on conserved bacteriocin motifs (e.g., "pediocin box" for *k*=14–19) and cited prior studies. Added scalability details: Experiments were conducted on Google Colab. Results: A comparison was made with alternative models. However, it was developed in more depth in the discussion section. Discussion : While our model demonstrates robust performance on the current dataset, its scalability to larger datasets requires further empirical validation. Although computational costs are expected to increase linearly with sequence length due to our fixed-dimension k-mer/embedding pipeline, real-world performance may vary with dataset diversity. Furthermore, while we recognize the importance of experimental validation through biological testing, this remains an area for future research as it falls beyond the scope of our current computational study Conclusions : We have moderated our claims by emphasizing key limitations, including dependence on public datasets (which may introduce gaps in LAB diversity representation) and the need for experimental validation in future work. We sincerely appreciate your constructive feedback. We have addressed all raised concerns through the following revisions: Abstract : Added limitations regarding computational costs and class imbalance to assess real-world feasibility. Introduction : Expanded on limitations of existing deep learning models. Discussed potential biases from selecting specific LAB genera (e.g., underrepresentation of rare bacteriocin producers). Methods : Clarified hyperparameter tuning. Justified k-mer lengths based on conserved bacteriocin motifs (e.g., "pediocin box" for *k*=14–19) and cited prior studies. Added scalability details: Experiments were conducted on Google Colab. Results: A comparison was made with alternative models. However, it was developed in more depth in the discussion section. Discussion : While our model demonstrates robust performance on the current dataset, its scalability to larger datasets requires further empirical validation. Although computational costs are expected to increase linearly with sequence length due to our fixed-dimension k-mer/embedding pipeline, real-world performance may vary with dataset diversity. Furthermore, while we recognize the importance of experimental validation through biological testing, this remains an area for future research as it falls beyond the scope of our current computational study Conclusions : We have moderated our claims by emphasizing key limitations, including dependence on public datasets (which may introduce gaps in LAB diversity representation) and the need for experimental validation in future work. Competing Interests: The authors declare that they have no competing interests. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 30 Aug 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 2 (revision) 20 Jun 25 read read read read Version 1 30 Aug 24 read read John J. Georrge , University of North Bengal, Darjeeling, India Alaa Kareem Niamah , University of Basrah, Basrah, Iraq Ismail Erol , Bahcesehir University, Istanbul, Turkey Cristóbal Joel González-Pérez , Centre for Food Research and Development, Hermosillo, Mexico Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Georrge J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Aug 2025 | for Version 2 John J. Georrge , University of North Bengal, Darjeeling, India 0 Views copyright © 2025 Georrge J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors revised the manuscript as per the reviewers comments. The article may be indexed. Competing Interests No competing interests were disclosed. Reviewer Expertise Rational Drug Designing, Novel Drug Target Identification, Cheminformatics, Bioinformatics, Molecular Modelling, Docking, QSAR, Pharmacohpore, Protein Engineering, Bioactive Peptides, Reverse Vaccinology, Artificial Intelligence, and Machine Learning. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Georrge JJ. Peer Review Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.183776.r393480) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-981/v2#referee-response-393480 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 González-Pérez C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Aug 2025 | for Version 2 Cristóbal Joel González-Pérez , Centre for Food Research and Development, Hermosillo, Sonora, Mexico 0 Views copyright © 2025 González-Pérez C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In this research, a deep learning neural network was developed for bacteriocin sequence classification, which distinguishes bacteriocins produced by LAB from those produced by non-LAB. I consider this relevant because currently, we lack tools to support these bioinformatics searches. Suggestions: TABLE 1. In examples of Class II, change Pediocina for Pediocin A or Pediocin. TABLE 1. In examples of Class IV, eliminate one “e” in Eenterocin. In Fields where bacteriocins can be applied to address diverse issues, in the subtitle Medicine, it is important to mention that no bacteriocin is currently being used due to the long process that must be gone through; they are important promising molecules but none have been accepted for application in humans. In page 5, in the last parragraph, check “BACII?”, change or check “?” for “α”. In the statistical analysis, the authors must mention whether the data are normal or not, that is, if they performed normality tests, they must be mentioned, since Tukey's is for parametric data. In the second paragraph, page 19, “in vitro” must be italic. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Microbiology (Bacteriocins) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) González-Pérez CJ. Peer Review Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.183776.r393916) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-981/v2#referee-response-393916 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Erol I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Aug 2025 | for Version 2 Ismail Erol , Bahcesehir University, Istanbul, Turkey 0 Views copyright © 2025 Erol I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions After revision, the authors substantially increased the quality of the manuscript. I have no futher comments. The current status of the manuscript is ready for indexing. Ensuring the reproducibility of research is really important, and the authors considered that and shared their code via Github. It is acknowledged that the authors have considered reproducibility. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Molecular Modeling I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Erol I. Peer Review Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.183776.r393911) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-981/v2#referee-response-393911 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Niamah A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Jul 2025 | for Version 2 Alaa Kareem Niamah , University of Basrah, Basrah, Basra Governorate, Iraq 0 Views copyright © 2025 Niamah A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions After a second review of the manuscript, I see that the authors have made the necessary corrections. I believe the manuscript is now clearer and ready for indexing. Competing Interests No competing interests were disclosed. Reviewer Expertise Bacteriocin production , Lactic acid Bacteria I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Niamah AK. Peer Review Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.183776.r393481) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-981/v2#referee-response-393481 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Niamah A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 Jan 2025 | for Version 1 Alaa Kareem Niamah , University of Basrah, Basrah, Basra Governorate, Iraq 0 Views copyright © 2025 Niamah A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Editors and Authors 1-The introduction needs to be supported by some sources related to the current study, such as: refer 1 and 2 ‏ 2-The aim of the study is not clear and is not mentioned in the manuscript introduction. 3-The labeling of the x-axis in Figure 2 should be corrected. 4-Table 2 has no meaning and should be deleted. Since the coding is known and previously included in references. 5-Figure 6 is not clear. It should be explained better than the current situation and explain what these shapes, such as circles and arrows, mean. 6-Table 8 should preferably be written and discussed with the results of previous references, not as in the current situation. It is not permissible to put a table containing the results of others. 7-Figure 8 The authors did not explain what A and B mean. 8-Figure 8 The authors did not explain what a and b mean. 9-The conclusions are very poor. This chapter is dedicated to the conclusions of the current study, but we see that the authors have mentioned many results. This chapter should be rewritten and all the results mentioned should be deleted. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Niamah A: Structure, mode of action and application of pediocin natural antimicrobial food preservative: A review. Basrah Journal of Agricultural Sciences . 2018; 31 (1): 59-69 Publisher Full Text 2. Niamah AK, Al-Sahlany STG, Verma DK, Shukla RM, et al.: Emerging lactic acid bacteria bacteriocins as anti-cancer and anti-tumor agents for human health. Heliyon . 2024; 10 (17): e37054 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Bacteriocin production , Lactic acid Bacteria I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 11 Sep 2025 Lady González, School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, 100119, Ecuador We thank you for your constructive comments. We have addressed all points as follows: The requested sources have been added to the introduction. The study's aim has been clarified in the introduction. We added explicit statement in the introduction's final paragraph. The caption for Figure 2 has been corrected. A more appropriate description was also given to the figure. Table 2 has been removed. A clearer explanation of Figure 6 has been added. Table 8 was misplaced; it has now been moved to the discussion section. Added an explanation of what (a) and (b) represent in Figure 8. Added an explanation of what (a) and (b) represent in Figure 8. The conclusion has been rewritten. Aspects such as practical limitations (dataset bias), and future directions (experimental validation) were added in the conclusión. View more View less Competing Interests The authors declare that they have no competing interests. reply Respond Report a concern Niamah AK. Peer Review Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.169463.r355998) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-981/v1#referee-response-355998 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Georrge J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Jan 2025 | for Version 1 John J. Georrge , University of North Bengal, Darjeeling, India 0 Views copyright © 2025 Georrge J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Abstract The research investigates a deep learning neural network for the binary classification of bacteriocin sequences produced by lactic acid bacteria (LAB). Utilizing k-mers and vector embeddings for feature extraction, the study tested ten group combinations, concluding that the concatenated features of 5-mers, 7-mers, and embedding vectors (EV) yielded superior results. With k-fold cross-validation (k=30), the model achieved notable accuracy (91.47%), precision, recall, and F1 score (91%). These results demonstrate a promising approach for identifying bacteriocins, paving the way for applications in medicine, livestock, aquaculture, and food preservation. Weaknesses: The abstract lacks explicit mention of the practical challenges or limitations, such as computational expense or data imbalance, which are critical for assessing the feasibility of real-world applications. Introduction The study highlights the growing challenge of antibiotic resistance and the potential of bacteriocins as alternatives. LAB bacteriocins, recognized as GRAS (Generally Recognized as Safe) and QPS (Qualified Presumption of Safety), show promise for therapeutic and industrial applications. The introduction emphasizes the critical need for efficient classification methods, leveraging artificial intelligence (AI) and deep learning to address the limitations of traditional genomic tools. Existing methods like BAGEL and BLASTP rely heavily on sequence homology, often leading to false negatives. The study proposes a deep neural network as an innovative solution for identifying LAB-produced bacteriocins. Weaknesses: While the introduction effectively frames the problem, it does not provide sufficient detail on the limitations of existing deep learning models, nor does it address the potential biases introduced by selecting specific LAB genera. Methods The research employed amino acid (AA) sequences sourced from the UniProt database, filtered for lengths between 50 and 2000 AAs. LAB bacteriocin sequences were labelled as “BacLAB,” and non-LAB sequences as “Non-BacLAB.” Feature extraction was performed using k-mers of varying lengths (3, 5, 7, 15, and 20) and embedding vectors generated via a Gated Recurrent Unit (GRU)-based recurrent neural network (RNN). The concatenated features were inputs to a deep neural network (DNN) structured in four blocks with 13 layers. The model used Adam optimization, a mean absolute error loss function, and k-fold cross-validation (k=30). Statistical analyses included ANOVA and Tukey tests to validate performance metrics. Weaknesses: The methods section lacks clarity on how hyperparameters were tuned and does not justify the selection of specific k-mer lengths. Results The study identified five lists of 100 characteristic k-mers for each selected length. Performance metrics from cross-validation demonstrated that the concatenation of 5-mers, 7-mers, and embedding vectors (5-mers+7-mers+EV) achieved the best results with: Loss: 8.50% Accuracy: 91.47% Precision, Recall, F1 Score: 91.00% Significant differences in accuracy and loss were observed between groups. The confusion matrix revealed high sensitivity but lower specificity, highlighting potential areas for improvement. Compared to other machine learning models, this study’s approach exceeded accuracy benchmarks by 3-10%. Weaknesses: While the results are promising, the limited scope of comparison with other algorithms leaves room for a more robust benchmarking process. Discussion The superior performance of the 5-mers+7-mers+EV group is attributed to characteristic motifs in bacteriocins. The study’s findings align with known sequences in subclass IIa bacteriocins, such as YGNGVXC. The results underscore the importance of selecting appropriate k-mer lengths to balance specificity and sensitivity. However, limitations include the model’s higher false positive rate for non-BacLAB sequences, indicating room for refinement in feature selection. Additionally, comparisons with existing tools like BAGEL reveal the unique advantage of the proposed approach in distinguishing LAB-specific bacteriocins. Weaknesses: The discussion does not critically evaluate the computational cost and scalability of the model for larger datasets. It also overlooks the potential need for experimental validation of the predictions to confirm biological relevance. Conclusion The study successfully developed a deep learning-based classification model for LAB bacteriocins, achieving consistent results comparable to and sometimes exceeding existing methods. The identified k-mers and embeddings offer a robust foundation for future work in therapeutic, aquacultural, and industrial applications. While promising, further specificity and practical testing improvements are essential to validate and expand the model’s utility. Weaknesses: The conclusion is overly optimistic and does not sufficiently address the study’s limitations, such as the reliance on publicly available datasets that may not represent all bacteriocin-producing LAB. References References were cited appropriately within the research, highlighting the breadth of prior work on bacteriocins and machine learning applications. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Rational Drug Designing, Novel Drug Target Identification, Cheminformatics, Bioinformatics, Molecular Modelling, Docking, QSAR, Pharmacohpore, Protein Engineering, Bioactive Peptides, Reverse Vaccinology, Artificial Intelligence, and Machine Learning. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 11 Sep 2025 Lady González, School of Biological Sciences and Engineering, University Yachay Tech, Urcuqui, 100119, Ecuador We sincerely appreciate your constructive feedback. We have addressed all raised concerns through the following revisions: Abstract : Added limitations regarding computational costs and class imbalance to assess real-world feasibility. Introduction : Expanded on limitations of existing deep learning models. Discussed potential biases from selecting specific LAB genera (e.g., underrepresentation of rare bacteriocin producers). Methods : Clarified hyperparameter tuning. Justified k-mer lengths based on conserved bacteriocin motifs (e.g., "pediocin box" for *k*=14–19) and cited prior studies. Added scalability details: Experiments were conducted on Google Colab. Results: A comparison was made with alternative models. However, it was developed in more depth in the discussion section. Discussion : While our model demonstrates robust performance on the current dataset, its scalability to larger datasets requires further empirical validation. Although computational costs are expected to increase linearly with sequence length due to our fixed-dimension k-mer/embedding pipeline, real-world performance may vary with dataset diversity. Furthermore, while we recognize the importance of experimental validation through biological testing, this remains an area for future research as it falls beyond the scope of our current computational study Conclusions : We have moderated our claims by emphasizing key limitations, including dependence on public datasets (which may introduce gaps in LAB diversity representation) and the need for experimental validation in future work. View more View less Competing Interests The authors declare that they have no competing interests. reply Respond Report a concern Georrge JJ. Peer Review Report For: Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :981 ( https://doi.org/10.5256/f1000research.169463.r355994) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-981/v1#referee-response-355994 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Deep learning neural network development...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/13-981/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/13-981/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/13-981/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('González LL et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/13-981/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/13-981", templates : { twitter : "Deep learning neural network development for the classification.... González LL et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/13-981/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/154432/169463") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "169463"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "329223": 0, "329222": 0, "329229": 0, "329228": 0, "329231": 0, "329230": 0, "329225": 0, "329224": 0, "393480": 6, "329227": 0, "393481": 4, "329226": 0, "355991": 0, "355997": 0, "355996": 0, "355999": 0, "355998": 20, "355993": 0, "355992": 0, "355995": 0, "355994": 19, "326316": 0, "326317": 0, "326318": 0, "326319": 0, "326315": 0, "393910": 0, "326324": 0, "393911": 2, "393909": 0, "326320": 0, "326321": 0, "326322": 0, "326323": 0, "393916": 5, "393917": 0, "393914": 0, "393915": 0, "393912": 0, "393913": 0, "348885": 0, "348886": 0, "345553": 0, "345554": 0, "322919": 0, "322924": 0, "322925": 0, "322926": 0, "322927": 0, "322920": 0, "322921": 0, "322922": 0, "322923": 0, "319220": 0, "319221": 0, "332788": 0, "319222": 0, "319223": 0, "319216": 0, "332785": 0, "322928": 0, "319217": 0, "332784": 0, "319218": 0, "332787": 0, "319219": 0, "332786": 0, "319224": 0, "319225": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "77cb08d0-9d34-4318-bab9-6391e2cfda99"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });