Enhanced Protein Complex Detection Using Square Clustering Coefficient

Enhanced Protein Complex Detection Using Square Clustering Coefficient | 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 Enhanced Protein Complex Detection Using Square Clustering Coefficient Parimah Mirzaee, nasrollah moghaddam charkari, Mehdy Roayaei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4312105/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 May, 2025 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract Identifying protein complexes from protein-protein interaction networks is one of the crucial tasks in computational biology. Traditional methods, along with their shortcomings in fully understanding protein complex composition, also have inherent limitations and are expensive to implement. In this paper, we introduce a novel method that not only acknowledges but actively tackles these challenges. Our approach, centered around a core-attachment framework, employs a blend of topological metrics, such as square clustering coefficients, in conjunction with traditional clustering coefficients. After establishing the core, we incorporate attachment proteins based on specific conditions employing a based depth-first approach to form a protein complex. By harnessing multiple metrics, our goal is to elevate the accuracy of protein complex identification beyond what single-metric approaches can achieve. To validate the effectiveness of our approach, we conducted extensive experiments using multiple datasets, including Gavin06, Krogan core, Krogan extend, and DIP datasets, and assessed metrics such as precision, recall, F-measure, and coverage. Our results not only demonstrate the superiority of our method over traditional approaches but also align with findings from related studies. Overall, our study contributes to the ongoing efforts in computational biology by presenting a comprehensive approach to protein complex identification that addresses the shortcomings of previous methods. Through a combination of innovative techniques and insights from recent research, we aim to push the boundaries of accuracy and comprehensiveness in protein complex detection. Protein complex Core-attachment structure Square clustering coeffcient Full Text Cite Share Download PDF Status: Published Journal Publication published 13 May, 2025 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 29 May, 2024 Reviewers invited by journal 20 May, 2024 Editor assigned by journal 11 May, 2024 First submitted to journal 07 May, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4312105","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304427940,"identity":"d7a3470a-0df9-41a2-aca0-f23d2346a44e","order_by":0,"name":"Parimah Mirzaee","email":"","orcid":"","institution":"Tarbiat Modares University Faculty of Electrical and Computer Engineering","correspondingAuthor":false,"prefix":"","firstName":"Parimah","middleName":"","lastName":"Mirzaee","suffix":""},{"id":304427941,"identity":"5d7bc3e9-131b-46ac-8054-42dfaac14a4d","order_by":1,"name":"nasrollah moghaddam charkari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACCQYGxgYGBh5+CBsKEojRItlAqhYGgwPIWvAB/tnNzz7OqLknY3zt8MEbP/fY2DOwH37A8HAPHkvuHDOeueFYMY/Z7bRky55naYkNPGkGDAnPcGsxkEgwZnzAlgDUkmMmwXPgMNAXOUC/HMCnJf0z44N/CTzGs3PMJP8c+G/PwP+GkJYcY8aNbQk8BtI5ZtI8Bw4wNkgQsEXiRk4x48y+BB4JoF+sZQ4kJ7ZJPDM4gE8L/4z0zYw93xLs+WcnH7z55oCdPT9/8sOHP/BowQRsQEyShlEwCkbBKBgFmAAA0plO5sEjexEAAAAASUVORK5CYII=","orcid":"","institution":"Tarbiat Modares University Faculty of Electrical and Computer Engineering","correspondingAuthor":true,"prefix":"","firstName":"nasrollah","middleName":"moghaddam","lastName":"charkari","suffix":""},{"id":304427942,"identity":"8cbec246-3dbd-4d54-af9a-287ff2e3b665","order_by":2,"name":"Mehdy Roayaei","email":"","orcid":"","institution":"Tarbiat Modares University Faculty of Electrical and Computer Engineering","correspondingAuthor":false,"prefix":"","firstName":"Mehdy","middleName":"","lastName":"Roayaei","suffix":""}],"badges":[],"createdAt":"2024-04-23 12:48:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4312105/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4312105/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-025-10615-x","type":"published","date":"2025-05-13T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067784,"identity":"fc24c857-efec-416d-8919-804bf612ab8e","added_by":"auto","created_at":"2025-05-19 16:06:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":404851,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancedProteinComplexDetectionnParimahMirzaee.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4312105/v1_covered_cb2d650e-061e-407e-a10b-743110cb71c1.pdf"}],"financialInterests":"","formattedTitle":"Enhanced Protein Complex Detection Using Square Clustering Coefficient","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Protein complex, Core-attachment structure, Square clustering coeffcient","lastPublishedDoi":"10.21203/rs.3.rs-4312105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4312105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Identifying protein complexes from protein-protein interaction networks is one of the crucial tasks in computational biology. Traditional methods, along with their shortcomings in fully understanding protein complex composition, also have inherent limitations and are expensive to implement. In this paper, we introduce a novel method that not only acknowledges but actively tackles these challenges. Our approach, centered around a core-attachment framework, employs a blend of topological metrics, such as square clustering coefficients, in conjunction with traditional clustering coefficients. After establishing the core, we incorporate attachment proteins based on specific conditions employing a based depth-first approach to form a protein complex. 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