{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T01:29:16Z","timestamp":1785461356243,"version":"3.56.0"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35 GM128765"],"award-info":[{"award-number":["R35 GM128765"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/pypi.org\/project\/geneplexus\/ and https:\/\/github.com\/krishnanlab\/PyGenePlexus.<\/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\/btad064","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T01:25:41Z","timestamp":1675214741000},"source":"Crossref","is-referenced-by-count":8,"title":["PyGenePlexus: a Python package for gene discovery using network-based machine learning"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3081-2758","authenticated-orcid":false,"given":"Christopher A","family":"Mancuso","sequence":"first","affiliation":[{"name":"Department of Computational Mathematics, Science and Engineering, Michigan State University , East Lansing, MI 48824, USA"},{"name":"Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Denver Anschutz Medical Campus , Aurora, CO 80045, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6025-6492","authenticated-orcid":false,"given":"Renming","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics, Science and Engineering, Michigan State University , East Lansing, MI 48824, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Michigan State University , East Lansing, MI 48824, USA"},{"name":"Department of Biomedical Informatics, University of Colorado-Denver Anschutz Medical Campus , Aurora, CO 80045, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"2023020606325439600_btad064-B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. 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