Disease Associated Protein-Protein Interaction Network Reconstruction Based on Comprehensive Influence Analysis

Disease Associated Protein-Protein Interaction Network Reconstruction Based on Comprehensive Influence Analysis | 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 Research article Disease Associated Protein-Protein Interaction Network Reconstruction Based on Comprehensive Influence Analysis Fei Zhu, Feifei Li, Xinghong Ling, Bairong Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-60999/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Proteins and their interactions are fundamental to biological systems and they are affecting our body. Functional study of protein networks is becoming increasingly essential to get a deep understanding of proteins and their roles in human life and diseases. Although several methods already exist for protein-protein interaction (PPI) network building, the precise reconstruction of disease associated PPI network remains a challenge. In this paper we introduce a novel concept of comprehensive influence of proteins in network, in which direct and indirect connections are adopted for the calculation of influential effects of a protein with different weights. With the optimized weights, we calculate and select the important proteins and their interactions to reconstruct the PPI network for further validation and confirmation. Results: To evaluate the performance of the method, we compared our model with six existing methods using five standard data sets. Conclusions: The results indicated that our method outperforms the existed ones. We then applied our model to prostate cancer and Parkinson’s disease to predict novel disease associated proteins for the future experimental validation. Bioinformatics Comprehensive influence analysis Disease associated protein prediction Network reconstruction Protein-protein interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Cite Share Download PDF Status: Posted Version 1 posted 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-60999","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":2144043,"identity":"0bfc83fc-8c16-4954-b4e5-f1aff3cf3b42","order_by":0,"name":"Fei Zhu","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Zhu","suffix":""},{"id":2144044,"identity":"bff89039-e57b-4f93-b8b7-b890e715b39a","order_by":1,"name":"Feifei Li","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feifei","middleName":"","lastName":"Li","suffix":""},{"id":2144045,"identity":"86d17aa0-8b19-4d9b-8469-e2c20ffa35fe","order_by":2,"name":"Xinghong Ling","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinghong","middleName":"","lastName":"Ling","suffix":""},{"id":2144046,"identity":"d4d4f905-bcf4-449d-aef5-2a458f94e40e","order_by":3,"name":"Bairong Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAwbGBghLAog/wASJ1sI4gzgtMADUwsxDjBZz9sNtHz7uqGWQn9187LHNH7vEBvbmbRIMNXdwarHsSWyeOfPMcQaDO8fSjXPbkhMbeI6VSTAce4bbYQcSm5l5244xGEjkmEnnNhxIbAAyJBgbDuPWcv4hRIv8jPxv0hZ/gFrk3xDQcgNsSw0Dw40cNmkGNpAtPPi1WM542Mw4s+0Aj8GNNDPJ3rZk4zaetGKLhGO4tZjzpz9m+NhWJyc/I/mZxI8/drL97Ic33vhQg1sLFBzmgTPZQEQCIQ0MDHWElYyCUTAKRsHIBQBgnFKMysLfiwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2899-1531","institution":"Sichuan University West China Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Bairong","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2020-08-17 11:46:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-60999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-60999/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":2346949,"identity":"c2509857-2bf9-418f-9985-00639d45607b","added_by":"auto","created_at":"2020-09-10 19:13:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53485,"visible":true,"origin":"","legend":"Performance of various methods for different network analysis.","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-60999/v1/Fig1.JPG"},{"id":2346950,"identity":"7102a67d-9c6c-43ff-9688-5dba4a3a5656","added_by":"auto","created_at":"2020-09-10 19:13:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47166,"visible":true,"origin":"","legend":"Overview of the prostate cancer PPI network.","description":"","filename":"Fig2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-60999/v1/Fig2.JPG"},{"id":2346951,"identity":"609eb548-a147-4bb6-b318-fa9f9e93a198","added_by":"auto","created_at":"2020-09-10 19:13:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70302,"visible":true,"origin":"","legend":"Representative central nodes. Nodes included AR(P10275), CDH1(P12830), and CHEK2(O96017) connected with several hub nodes in the network.","description":"","filename":"Fig3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-60999/v1/Fig3.JPG"},{"id":2346952,"identity":"c7f18d84-9e6c-4ee1-965f-15b55aafc01f","added_by":"auto","created_at":"2020-09-10 19:13:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17957,"visible":true,"origin":"","legend":"Probability of the node-degree distribution of the PPI network associated with prostate cancer and representative of a scale-free network.","description":"","filename":"Fig4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-60999/v1/Fig4.JPG"},{"id":2346953,"identity":"a884dcd0-9744-4aae-b416-7df2b78f33c7","added_by":"auto","created_at":"2020-09-10 19:13:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123215,"visible":true,"origin":"","legend":"Overview of the Parkinson’s disease PPI network.","description":"","filename":"Fig5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-60999/v1/Fig5.JPG"},{"id":2346954,"identity":"a30653c5-3fa6-470f-be09-c0f3e464ba7e","added_by":"auto","created_at":"2020-09-10 19:13:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":74018,"visible":true,"origin":"","legend":"Representative central nodes in the Parkinson’s disease PPI network. 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Functional study of protein networks is becoming increasingly essential to get a deep understanding of proteins and their roles in human life and diseases. Although several methods already exist for protein-protein interaction (PPI) network building, the precise reconstruction of disease associated PPI network remains a challenge. In this paper we introduce a novel concept of comprehensive influence of proteins in network, in which direct and indirect connections are adopted for the calculation of influential effects of a protein with different weights. With the optimized weights, we calculate and select the important proteins and their interactions to reconstruct the PPI network for further validation and confirmation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTo evaluate the performance of the method, we compared our model with six existing methods using five standard data sets.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe results indicated that our method outperforms the existed ones. We then applied our model to prostate cancer and Parkinson’s disease to predict novel disease associated proteins for the future experimental validation.\u003c/p\u003e","manuscriptTitle":"Disease Associated Protein-Protein Interaction Network Reconstruction Based on Comprehensive Influence Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-10 18:12:42","doi":"10.21203/rs.3.rs-60999/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5db1d02-79ef-4f20-8bc0-ae035655aa90","owner":[],"postedDate":"September 10th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":484617,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2020-10-08T20:05:47+00:00","versionOfRecord":[],"versionCreatedAt":"2020-09-10 18:12:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-60999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-60999","identity":"rs-60999","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}