DeEPsnap: human essential gene prediction by integrating multi-omics data

DeEPsnap: human essential gene prediction by integrating multi-omics data | 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 DeEPsnap: human essential gene prediction by integrating multi-omics data Xue Zhang, Weijia Xiao, Brent Cochran, Wangxin Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5390345/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Essential genes are necessary for the survival or reproduction of a living organism. The prediction and analysis of gene essentiality can advance our understanding of basic life and human diseases, and further boost the development of new drugs. Wet lab methods for identifying cell essential genes are often costly, time-consuming, and laborious. As a complement, computational methods have been proposed to predict essential genes by integrating multiple biological data sources. Most of these methods are evaluated on model organisms. However, prediction methods for human essential genes are still limited and the relationship between human gene essentiality and different biological information still needs to be explored. In addition, exploring suitable deep learning techniques to overcome the limitations of traditional machine learning methods and improve prediction accuracy is also important and interesting. We propose a snapshot ensemble deep neural network method, DeEPsnap, to predict human essential genes. DeEPsnap integrates sequence features derived from DNA and protein sequence data with features extracted or learned from multiple types of functional data, such as gene ontology, protein complex, protein domain, and protein-protein interaction network. More than 200 features from these biological data are extracted/learned which are integrated together to train a series of cost-sensitive deep neural networks by utilizing multiple deep learning techniques. The proposed snapshot mechanism enables us to train multiple models without increasing extra training effort and cost. The experimental results of 10-fold cross-validation show that DeEPsnap can accurately predict human gene essentiality with an average AUROC (Area Under the Receiver Operating Characteristic curve) of 96.1%, the average AUPRC (Area under the Precision-Recall curve) of 93.82%, the average accuracy of 92.21%, and the average F1 measure about 80.62%. In addition, the comparison of experimental results shows that DeEPsnap outperforms several popular traditional machine learning models and deep learning models. We have demonstrated that the proposed method, DeEPsnap, is effective for predicting human essential genes. Essential gene prediction deep learning gene ontology network embedding sequence feature protein complex protein domain snapshot ensemble Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Jan, 2025 Reviews received at journal 19 Dec, 2024 Reviewers agreed at journal 19 Dec, 2024 Reviews received at journal 10 Dec, 2024 Reviewers agreed at journal 20 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers invited by journal 18 Nov, 2024 Editor assigned by journal 18 Nov, 2024 Editor invited by journal 18 Nov, 2024 Submission checks completed at journal 16 Nov, 2024 First submitted to journal 04 Nov, 2024 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. 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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-5390345","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388460705,"identity":"0a141beb-1d87-4856-b385-9a5fbd2f89ed","order_by":0,"name":"Xue Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACPmYGBgkGHgkGNv7mA0C+hAxBLWwwLfwSxxJAWngIawEpAzEkG3IMQDQRWth5D974IWMhb3DgzOdXN2oseBjYDx/dgN9hfMmWPTwShhsO926zzjkGdBhPWtoN/Fp4zCR4eCQYNxw4u804hw2oRYLHjKAWyT88EvYbDuQ8M875R6QWaaAtiTMbcpgf57YRp8XYWoZHIrlf4pgZc26fBA8bIb/w858xvPm2p862jb/58eecb3Vy/OyHj+HVAgaMPRAbwRHERlA5GPwAk8wfiFM9CkbBKBgFIw0AAPYrPXWDXeD7AAAAAElFTkSuQmCC","orcid":"","institution":"Shaoyang University","correspondingAuthor":true,"prefix":"","firstName":"Xue","middleName":"","lastName":"Zhang","suffix":""},{"id":388460707,"identity":"9b0da7de-1a45-4303-8479-55170265e1ca","order_by":1,"name":"Weijia Xiao","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Weijia","middleName":"","lastName":"Xiao","suffix":""},{"id":388460709,"identity":"ebebbcd4-ba65-4135-9938-39772ff3d4dc","order_by":2,"name":"Brent Cochran","email":"","orcid":"","institution":"Tufts University","correspondingAuthor":false,"prefix":"","firstName":"Brent","middleName":"","lastName":"Cochran","suffix":""},{"id":388460710,"identity":"8661978e-9e52-4372-9a07-ed3283cab175","order_by":3,"name":"Wangxin Xiao","email":"","orcid":"","institution":"Shaoyang University","correspondingAuthor":false,"prefix":"","firstName":"Wangxin","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2024-11-04 19:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5390345/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5390345/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-99164-9","type":"published","date":"2025-07-21T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87756562,"identity":"49c183e1-b0c3-40c0-9c79-c576bb1b33c6","added_by":"auto","created_at":"2025-07-28 16:01:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1001588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript1025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5390345/v1_covered_c36904fc-c711-4ada-b51f-cdf2adc49f3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DeEPsnap: human essential gene prediction by integrating multi-omics data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Essential gene prediction, deep learning, gene ontology, network embedding, sequence feature, protein complex, protein domain, snapshot ensemble","lastPublishedDoi":"10.21203/rs.3.rs-5390345/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5390345/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Essential genes are necessary for the survival or reproduction of a living organism. The prediction and analysis of gene essentiality can advance our understanding of basic life and human diseases, and further boost the development of new drugs. Wet lab methods for identifying cell essential genes are often costly, time-consuming, and laborious. As a complement, computational methods have been proposed to predict essential genes by integrating multiple biological data sources. Most of these methods are evaluated on model organisms. However, prediction methods for human essential genes are still limited and the relationship between human gene essentiality and different biological information still needs to be explored. In addition, exploring suitable deep learning techniques to overcome the limitations of traditional machine learning methods and improve prediction accuracy is also important and interesting. We propose a snapshot ensemble deep neural network method, DeEPsnap, to predict human essential genes. 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