{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:50:28Z","timestamp":1785577828548,"version":"3.56.0"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Medicine, Robert Wood Johnson Medical School"},{"name":"Rutgers Institute for Health, Health Care Policy"},{"DOI":"10.13039\/100011132","name":"State University of New Jersey","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100011132","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.e. Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of IntelliGenes is available on GitHub (https:\/\/github.com\/drzeeshanahmed\/intelligenes) and Code Ocean (https:\/\/codeocean.com\/capsule\/8638596\/tree\/v1).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad755","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T22:33:13Z","timestamp":1702593193000},"source":"Crossref","is-referenced-by-count":160,"title":["<i>IntelliGenes<\/i>: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles"],"prefix":"10.1093","volume":"39","author":[{"given":"William","family":"DeGroat","sequence":"first","affiliation":[{"name":"Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey , New Brunswick, NJ 08901, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinesh","family":"Mendhe","sequence":"additional","affiliation":[{"name":"Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey , New Brunswick, NJ 08901, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atharva","family":"Bhusari","sequence":"additional","affiliation":[{"name":"Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey , New Brunswick, NJ 08901, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Habiba","family":"Abdelhalim","sequence":"additional","affiliation":[{"name":"Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey , New Brunswick, NJ 08901, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saman","family":"Zeeshan","sequence":"additional","affiliation":[{"name":"Rutgers Cancer Institute of New Jersey, Rutgers University , New Brunswick, NJ 08901, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7065-1699","authenticated-orcid":false,"given":"Zeeshan","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey , New Brunswick, NJ 08901, United States"},{"name":"Department of Medicine, Robert Wood Johnson Medical School, Rutgers Health , New Brunswick, NJ 08901, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"2023122205062129700_btad755-B1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/bs.pmbts.2022.02.002","article-title":"Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis","volume":"190","author":"Ahmed","year":"2022","journal-title":"Progress Mol Biol Transl Sci"},{"key":"2023122205062129700_btad755-B2","first-page":"baaa010","article-title":"Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine","volume":"2020","author":"Ahmed","year":"2020","journal-title":"Database J Biol Databases Curation"},{"key":"2023122205062129700_btad755-B3","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1126\/science.1156409","article-title":"Genetic mapping in human disease","volume":"322","author":"Altshuler","year":"2008","journal-title":"Science (New York, N.Y.)"},{"key":"2023122205062129700_btad755-B4","doi-asserted-by":"crossref","DOI":"10.1101\/2023.09.08.553995","article-title":"Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine","author":"DeGroat","year":"2023"},{"key":"2023122205062129700_btad755-B5","doi-asserted-by":"crossref","first-page":"100493","DOI":"10.1016\/j.simpa.2023.100493","article-title":"Hygieia: AI\/ML pipeline integrating healthcare and genomics data to investigate genes associated with targeted disorders and predict disease","volume":"16","author":"DeGroat","year":"2023","journal-title":"Softw Impacts"},{"key":"2023122205062129700_btad755-B6","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1097\/MIB.0000000000001222","article-title":"Machine learning-based gene prioritization identifies novel candidate risk genes for inflammatory bowel disease","volume":"23","author":"Isakov","year":"2017","journal-title":"Inflamm Bowel Dis"},{"key":"2023122205062129700_btad755-B7","doi-asserted-by":"crossref","first-page":"9617","DOI":"10.1038\/s41598-019-45989-0","article-title":"Machine learning approaches to predict lupus disease activity from gene expression data","volume":"9","author":"Kegerreis","year":"2019","journal-title":"Sci Rep"},{"key":"2023122205062129700_btad755-B8","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.cmpb.2019.04.008","article-title":"Statistical characterization and classification of Colon microarray gene expression data using multiple machine learning paradigms","volume":"176","author":"Maniruzzaman","year":"2019","journal-title":"Comput Methods Programs Biomed"},{"key":"2023122205062129700_btad755-B9","doi-asserted-by":"crossref","first-page":"e0251800","DOI":"10.1371\/journal.pone.0251800","article-title":"Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data","volume":"16","author":"Schaack","year":"2021","journal-title":"PLoS One"},{"key":"2023122205062129700_btad755-B10","doi-asserted-by":"crossref","first-page":"bbac191","DOI":"10.1093\/bib\/bbac191","article-title":"Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine","volume":"23","author":"Vadapalli","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023122205062129700_btad755-B11","doi-asserted-by":"crossref","first-page":"110584","DOI":"10.1016\/j.ygeno.2023.110584","article-title":"Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine","volume":"115","author":"Venkat","year":"2023","journal-title":"Genomics"},{"key":"2023122205062129700_btad755-B12","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ajhg.2017.06.005","article-title":"10 Years of GWAS discovery: biology, function, and translation","volume":"101","author":"Visscher","year":"2017","journal-title":"Am J Hum Genet"},{"key":"2023122205062129700_btad755-B13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ajhg.2022.12.004","article-title":"Genotype first: clinical genomics research through a reverse phenotyping approach","volume":"110","author":"Wilczewski","year":"2023","journal-title":"Am J Hum Genet"},{"key":"2023122205062129700_btad755-B14","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1093\/bib\/bbz038","article-title":"100 years of evolving gene\u2013disease complexities and scientific debutants","volume":"21","author":"Zeeshan","year":"2020","journal-title":"Brief Bioinform"},{"key":"2023122205062129700_btad755-B15","doi-asserted-by":"crossref","first-page":"645998","DOI":"10.3389\/fnins.2021.645998","article-title":"Identification of diagnostic markers for major depressive disorder using machine learning methods","volume":"15","author":"Zhao","year":"2021","journal-title":"Front Neurosci"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btad755\/54450281\/btad755.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/12\/btad755\/54752921\/btad755.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/12\/btad755\/54752921\/btad755.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T05:06:42Z","timestamp":1703221602000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btad755\/7473370"}},"subtitle":[],"editor":[{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,12,1]]},"references-count":15,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad755","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,12,1]]},"published":{"date-parts":[[2023,12,1]]},"article-number":"btad755"}}