{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T05:58:16Z","timestamp":1776059896768,"version":"3.50.1"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T00:00:00Z","timestamp":1693008000000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute on Minority Health"},{"name":"Health Disparities of the National Institutes of Health","award":["K99MD016964"],"award-info":[{"award-number":["K99MD016964"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Advances in technology have generated larger omics datasets with potential applications for machine learning. In many datasets, however, cost and limited sample availability result in an excessively higher number of features as compared to observations. Moreover, biological processes are associated with networks of core and peripheral genes, while traditional feature selection approaches capture only core genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To overcome these limitations, we present dRFEtools that implements dynamic recursive feature elimination (RFE), reducing computational time with high accuracy compared to standard RFE, expanding dynamic RFE to regression algorithms, and outputting the subsets of features that hold predictive power with and without peripheral features. dRFEtools integrates with scikit-learn (the popular Python machine learning platform) and thus provides new opportunities for dynamic RFE in large-scale omics data while enhancing its interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>dRFEtools is freely available on PyPI at https:\/\/pypi.org\/project\/drfetools\/ or on GitHub https:\/\/github.com\/LieberInstitute\/dRFEtools, implemented in Python 3, and supported on Linux, Windows, and Mac OS.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad513","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T01:04:42Z","timestamp":1692925482000},"source":"Crossref","is-referenced-by-count":20,"title":["dRFEtools: dynamic recursive feature elimination for omics"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2016-4646","authenticated-orcid":false,"given":"Kynon J M","family":"Benjamin","sequence":"first","affiliation":[{"name":"Lieber Institute for Brain Development , Baltimore, MD 21205, United States"},{"name":"Department of Neurology, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"}]},{"given":"Tarun","family":"Katipalli","sequence":"additional","affiliation":[{"name":"Lieber Institute for Brain Development , Baltimore, MD 21205, United States"}]},{"given":"Apu\u00e3 C M","family":"Paquola","sequence":"additional","affiliation":[{"name":"Lieber Institute for Brain Development , Baltimore, MD 21205, United States"},{"name":"Department of Neurology, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"}]}],"member":"286","published-online":{"date-parts":[[2023,8,26]]},"reference":[{"key":"2023090105164783600_btad513-B05860538","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1016\/j.cell.2017.05.038","article-title":"An expanded view of complex traits: From polygenic to omnigenic","volume":"169","author":"Boyle","year":"2017","journal-title":"Cell"},{"key":"2023090105164783600_btad513-B1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neuron.2019.05.013","article-title":"Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia","volume":"103","author":"Collado-Torres","year":"2019","journal-title":"Neuron"},{"key":"2023090105164783600_btad513-B2","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1038\/s41593-018-0197-y","article-title":"Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis","volume":"21","author":"Jaffe","year":"2018","journal-title":"Nat. Neurosci"},{"key":"2023090105164783600_btad513-B5707130","doi-asserted-by":"crossref","DOI":"10.3390\/proteomes4030028","article-title":"Omics\"-informed drug and biomarker discovery: Opportunities, challenges and future perspectives","volume":"4","author":"Matthews","year":"2016","journal-title":"Proteomes"},{"key":"2023090105164783600_btad513-B4","author":"Nguyen","year":"2006"},{"key":"2023090105164783600_btad513-B5","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1016\/j.metabol.2018.08.002","article-title":"Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics","volume":"87","author":"Perakakis","year":"2018","journal-title":"Metabolism"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btad513\/51273246\/btad513.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/8\/btad513\/51321108\/btad513.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/8\/btad513\/51321108\/btad513.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T01:57:39Z","timestamp":1693533459000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btad513\/7252233"}},"subtitle":[],"editor":[{"given":"Peter","family":"Robinson","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":6,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad513","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.07.27.501227","asserted-by":"object"}]},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,8,1]]},"published":{"date-parts":[[2023,8,1]]},"article-number":"btad513"}}