{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:11:22Z","timestamp":1767175882447,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011249","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000}}],"reference-count":84,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003968","name":"Iran National Science Foundation","doi-asserted-by":"publisher","award":["96006077"],"award-info":[{"award-number":["96006077"]}],"id":[{"id":"10.13039\/501100003968","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012481","name":"University of New South Wales Canberra","doi-asserted-by":"publisher","award":["DE220101210"],"award-info":[{"award-number":["DE220101210"]}],"id":[{"id":"10.13039\/100012481","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE220101210"],"award-info":[{"award-number":["DE220101210"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Welcome","award":["WT223718\/Z\/21\/Z"],"award-info":[{"award-number":["WT223718\/Z\/21\/Z"]}]},{"name":"UNSW Scientia Program Fellowship"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene drives disease, known as \u201cgene prioritization,\u201d is not entirely understood. Genome-wide searches for gene-disease associations are still underdeveloped due to reliance on previous discoveries and evidence sources with false positive or negative relations. This paper introduces DeepGenePrior, a model based on deep neural networks that prioritizes candidate genes in genetic diseases. Using the well-studied Variational AutoEncoder (VAE), we developed a score to measure the impact of genes on target diseases. Unlike other methods that use prior data to select candidate genes, based on the \"guilt by association\" principle and auxiliary data sources like protein networks, our study exclusively employs copy number variants (CNVs) for gene prioritization. By analyzing CNVs from 74,811 individuals with autism, schizophrenia, and developmental delay, we identified genes that best distinguish cases from controls. Our findings indicate a 12% increase in fold enrichment in brain-expressed genes compared to previous studies and a 15% increase in genes associated with mouse nervous system phenotypes. Furthermore, we identified common deletions in\n                    <jats:italic>ZDHHC8<\/jats:italic>\n                    ,\n                    <jats:italic>DGCR5<\/jats:italic>\n                    , and\n                    <jats:italic>CATG00000022283<\/jats:italic>\n                    among the top genes related to all three disorders, suggesting a common etiology among these clinically distinct conditions. DeepGenePrior is publicly available online at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/git.dml.ir\/z_rahaie\/DGP\" xlink:type=\"simple\">http:\/\/git.dml.ir\/z_rahaie\/DGP<\/jats:ext-link>\n                    to address obstacles in existing gene prioritization studies identifying candidate genes.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1011249","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T13:27:18Z","timestamp":1690205238000},"page":"e1011249","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants"],"prefix":"10.1371","volume":"19","author":[{"given":"Zahra","family":"Rahaie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9835-4493","authenticated-orcid":true,"given":"Hamid R.","family":"Rabiee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamid","family":"Alinejad-Rokny","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"pcbi.1011249.ref001","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.procs.2018.10.411","article-title":"Analysis of computational gene prioritization approaches","volume":"143","author":"MR Raj","year":"2018","journal-title":"Procedia computer science"},{"issue":"5","key":"pcbi.1011249.ref002","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/TST.2015.7297749","article-title":"Computational approaches for prioritizing candidate disease genes based on PPI networks","volume":"20","author":"W Lan","year":"2015","journal-title":"Tsinghua Science and Technology"},{"issue":"13","key":"pcbi.1011249.ref003","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1093\/bioinformatics\/bty079","article-title":"pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion","volume":"34","author":"AA Kumar","year":"2018","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1011249.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-11-460","article-title":"Candidate gene prioritization by network analysis of differential expression using machine learning approaches","volume":"11","author":"D Nitsch","year":"2010","journal-title":"BMC bioinformatics"},{"issue":"7","key":"pcbi.1011249.ref005","doi-asserted-by":"crossref","first-page":"e39932","DOI":"10.1371\/journal.pone.0039932","article-title":"Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data","volume":"7","author":"E Glaab","year":"2012","journal-title":"PloS one"},{"key":"pcbi.1011249.ref006","unstructured":"Baldi P. 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