{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T22:08:53Z","timestamp":1787004533961,"version":"build-2736575974"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011773","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000}}],"reference-count":58,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35 GM128765"],"award-info":[{"award-number":["R35 GM128765"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["F32 GM134595"],"award-info":[{"award-number":["F32 GM134595"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011773","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T13:32:25Z","timestamp":1704893545000},"page":"e1011773","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Joint representation of molecular networks from multiple species improves gene classification"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3081-2758","authenticated-orcid":true,"given":"Christopher A.","family":"Mancuso","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0889-5705","authenticated-orcid":true,"given":"Kayla A.","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6025-6492","authenticated-orcid":true,"given":"Renming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7980-4110","authenticated-orcid":true,"given":"Arjun","family":"Krishnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"pcbi.1011773.ref001","doi-asserted-by":"crossref","first-page":"W214","DOI":"10.1093\/nar\/gkq537","article-title":"The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function","volume":"38","author":"D Warde-Farley","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1011773.ref002","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1101\/gr.071852.107","article-title":"Protein networks in disease","volume":"18","author":"T Ideker","year":"2008","journal-title":"Genome Res"},{"key":"pcbi.1011773.ref003","doi-asserted-by":"crossref","first-page":"e1000641","DOI":"10.1371\/journal.pcbi.1000641","article-title":"Associating Genes and Protein Complexes with Disease via Network Propagation.","volume":"6","author":"O Vanunu","year":"2010","journal-title":"PLOS Comput Biol."},{"key":"pcbi.1011773.ref004","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1093\/bioinformatics\/btaa150","article-title":"Supervised learning is an accurate method for network-based gene classification","volume":"36","author":"R Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"pcbi.1011773.ref005","first-page":"114","article-title":"Lost in translation: animal models and clinical trials in cancer treatment","volume":"6","author":"IW Mak","year":"2014","journal-title":"Am J Transl Res"},{"key":"pcbi.1011773.ref006","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1186\/s12967-019-1976-2","article-title":"Animal to human translation: a systematic scoping review of reported concordance rates.","volume":"17","author":"CHC Leenaars","year":"2019","journal-title":"J Transl Med"},{"key":"pcbi.1011773.ref007","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1111\/j.1476-5381.2011.01811.x","article-title":"Concordance of preclinical and clinical pharmacology and toxicology of therapeutic monoclonal antibodies and fusion proteins: cell surface targets","volume":"166","author":"PJ Bugelski","year":"2012","journal-title":"Br J Pharmacol"},{"key":"pcbi.1011773.ref008","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/nri3192","article-title":"The storm has cleared: lessons from the CD28 superagonist TGN1412 trial","volume":"12","author":"T. 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