{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:44:16Z","timestamp":1775673856374,"version":"3.50.1"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T00:00:00Z","timestamp":1508716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["ES025632, ES023485, ES019776, HL095479, EY022618"],"award-info":[{"award-number":["ES025632, ES023485, ES019776, HL095479, EY022618"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Furthermore, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, a software for data integration, network visualization, clustering, and differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/kuppal.shinyapps.io\/xmwas (Online) and https:\/\/github.com\/kuppal2\/xMWAS\/ (R)<\/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\/btx656","type":"journal-article","created":{"date-parts":[[2017,10,17]],"date-time":"2017-10-17T19:11:49Z","timestamp":1508267509000},"page":"701-702","source":"Crossref","is-referenced-by-count":169,"title":["xMWAS: a data-driven integration and differential network analysis tool"],"prefix":"10.1093","volume":"34","author":[{"given":"Karan","family":"Uppal","sequence":"first","affiliation":[{"name":"Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA, USA"}]},{"given":"Chunyu","family":"Ma","sequence":"additional","affiliation":[{"name":"Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA, USA"}]},{"given":"Young-Mi","family":"Go","sequence":"additional","affiliation":[{"name":"Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA, USA"}]},{"given":"Dean P","family":"Jones","sequence":"additional","affiliation":[{"name":"Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,10,23]]},"reference":[{"key":"2023012712381929200_btx656-B1","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: a network-based approach to human disease","volume":"12","author":"Barabasi","year":"2011","journal-title":"Nat. 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