{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:56Z","timestamp":1772138036617,"version":"3.50.1"},"reference-count":94,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T00:00:00Z","timestamp":1583280000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"US National Institutes of Health","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R35 GM128765"],"award-info":[{"award-number":["R35 GM128765"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MSU start-up funds"},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["F32 F32GM134595"],"award-info":[{"award-number":["F32 F32GM134595"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MSU Engineering Distinguished Fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene\u2019s full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation\u2019s appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Contact<\/jats:title>\n                    <jats:p>arjun@msu.edu<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa150","type":"journal-article","created":{"date-parts":[[2020,2,27]],"date-time":"2020-02-27T15:16:34Z","timestamp":1582816594000},"page":"3457-3465","source":"Crossref","is-referenced-by-count":33,"title":["Supervised learning is an accurate method for network-based gene classification"],"prefix":"10.1093","volume":"36","author":[{"given":"Renming","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computational Mathematics , Science and Engineering"}]},{"given":"Christopher A","family":"Mancuso","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics , Science and Engineering"}]},{"given":"Anna","family":"Yannakopoulos","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics , Science and Engineering"}]},{"given":"Kayla A","family":"Johnson","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics , Science and Engineering"},{"name":"Department of Biochemistry and Molecular Biology , Michigan State University, East Lansing, MI 48824, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7980-4110","authenticated-orcid":false,"given":"Arjun","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics , Science and Engineering"},{"name":"Department of Biochemistry and Molecular Biology , Michigan State University, East Lansing, MI 48824, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"2023062312015815100_btaa150-B1","doi-asserted-by":"crossref","first-page":"i901","DOI":"10.1093\/bioinformatics\/bty559","article-title":"Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes","volume":"34","author":"Alshahrani","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062312015815100_btaa150-B2","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J. 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