A Multi-Label Cascade Flexible Neural Forest Model for Predicting the Subcellular Location of Multi-site Bacterial Proteins

A Multi-Label Cascade Flexible Neural Forest Model for Predicting the Subcellular Location of Multi-site Bacterial Proteins | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Multi-Label Cascade Flexible Neural Forest Model for Predicting the Subcellular Location of Multi-site Bacterial Proteins Lianxin Zhong, Yang Li, Yanhui Cheng, Guangchang Han, Anqi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8537189/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The study of subcellular localization of multi-site proteins provided an extremely important reference value for understanding the pathogenesis of diseases, drug design and disease prevention. At present, the multi-label learning models used for the subcellular localization of multi-site proteins frequently suffered from low prediction accuracy and inability to accurately localize protein sequences with low similarity. In this paper, a multi-label cascade flexible neural forest (MLCFN Forest) model was proposed to accomplish the subcellular localization of multi-site proteins. The model maximized the retention of inter-label correlation by “coding-classification-decoding” protein labels. The proposed multi-label model used flexible neural tree (FNT) as the basic learner, which can automatically determine its own network structure during model training. By introducing "FNT Group", it broke through the limitation of the single output structure in FNT. Finally, the proposed model used a layer-by-layer widening hierarchical processing framework, which not only improved the prediction performance of the model, but also avoided the waste of model structure and algorithm calculation as much as possible. Experiments on Gram-negative bacteria and Gram-positive bacteria data sets and tests in low-dimensional sample space showed that the proposed model can effectively improve the prediction accuracy of multi-site protein subcellular localization. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Multi-label protein subcellular localization Cascade forest Multi-label learning Ensemble model Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 24 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor invited by journal 24 Feb, 2026 Editor assigned by journal 08 Jan, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8537189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":597824227,"identity":"55342cfb-6080-4e12-b2b4-9f7cfaddea20","order_by":0,"name":"Lianxin Zhong","email":"","orcid":"","institution":"Taishan College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lianxin","middleName":"","lastName":"Zhong","suffix":""},{"id":597824229,"identity":"86dd120e-8e59-4334-b02a-7b391c2f5cc2","order_by":1,"name":"Yang Li","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":597824231,"identity":"f0a7748f-2785-4920-bda6-746a4a80f04a","order_by":2,"name":"Yanhui Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PIQvCQBTA8TuErRysvjHQrzA5GCY/i4dh5RiKxSByh6BFsE4YfgYtVm/FpNlFp3XFbtC5YtoWBe8f7njwfhyHkE73g7kKGe+z28sHLMbQbNUk/ZxgKU4d2hYVxBY5QXFB5HzMkKoglnk+3slABZazuKTrDfSwaKS3pIQYJPApcdXIjk5DudtDYCKDUl5GEPecN2HbhDOZ7mGEBTGcUmJlBTl8SARMqCoCn1ditgU/ljtRi2SeHbl9FiYcr8Mj0Pas6i8W9yB7dtkq9K+P5WTabJmz9F5GviJucTfqreeZ1/q7Op1O91e9ANS3TquIv03zAAAAAElFTkSuQmCC","orcid":"","institution":"Taishan College of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Cheng","suffix":""},{"id":597824234,"identity":"f949a420-8cca-4467-8914-075feb275d47","order_by":3,"name":"Guangchang Han","email":"","orcid":"","institution":"Taishan College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guangchang","middleName":"","lastName":"Han","suffix":""},{"id":597824235,"identity":"64e2e3bd-0685-4dd6-b3e7-a7a0db0c2c26","order_by":4,"name":"Anqi Li","email":"","orcid":"","institution":"Taishan College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Anqi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-07 05:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8537189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8537189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104400770,"identity":"3de6b644-69bb-4f39-9f8a-39fec4abf0e4","added_by":"auto","created_at":"2026-03-11 12:10:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":807858,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8537189/v1_covered_101ce41e-792a-4278-ad54-5f489c443b59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Label Cascade Flexible Neural Forest Model for Predicting the Subcellular Location of Multi-site Bacterial Proteins","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multi-label protein subcellular localization, Cascade forest, Multi-label learning, Ensemble model, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-8537189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8537189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study of subcellular localization of multi-site proteins provided an extremely important reference value for understanding the pathogenesis of diseases, drug design and disease prevention. At present, the multi-label learning models used for the subcellular localization of multi-site proteins frequently suffered from low prediction accuracy and inability to accurately localize protein sequences with low similarity. In this paper, a multi-label cascade flexible neural forest (MLCFN Forest) model was proposed to accomplish the subcellular localization of multi-site proteins. The model maximized the retention of inter-label correlation by \u0026ldquo;coding-classification-decoding\u0026rdquo; protein labels. The proposed multi-label model used flexible neural tree (FNT) as the basic learner, which can automatically determine its own network structure during model training. By introducing \"FNT Group\", it broke through the limitation of the single output structure in FNT. 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