Integrating Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

Integrating Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer | 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 Integrating Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer Hao Wu, Zhong-Li Chen, Ying-Fu Wu, Hong-Ming Zhang, Quan-Zhong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-482282/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Sep, 2021 Read the published version in Interdisciplinary Sciences: Computational Life Sciences → Version 1 posted You are reading this latest preprint version Abstract Background: With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data such as the cancer genome atlas ICGC and TCGA, it gains increasing importance how to detect the functional high-frequency mutation gene set in cells that causes cancer within the eld of medicine. Methods: In this study, to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules, we propose a new recognition method of driver modules, named ECSWalk, based on the human protein interaction networks and pan-cancer somatic mutation data. This study rstly utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Secondly, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are reasonably split using the way of the induced subgraph, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules. Results: This method is applied to the analysis of TCGA pan-cancer data, and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2. Conclusions: ECSWalk is of theoretical guidance and practical value for cancer diagnosis, treatment and drug targets. Medical Informatics driver modules node similarity random walk with restart complex networks Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF. Cite Share Download PDF Status: Published Journal Publication published 07 Sep, 2021 Read the published version in Interdisciplinary Sciences: Computational Life Sciences → 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. 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-482282","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":24367154,"identity":"7019e4c1-ea6e-4e51-a3d8-bc25e8d30762","order_by":0,"name":"Hao 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