{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:42Z","timestamp":1760245062902,"version":"3.37.3"},"reference-count":5,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T00:00:00Z","timestamp":1614297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["Z190004"],"award-info":[{"award-number":["Z190004"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11971404"],"award-info":[{"award-number":["11971404"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Scientific Project","award":["71988101"],"award-info":[{"award-number":["71988101"]}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["B13028"],"award-info":[{"award-number":["B13028"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1916251"],"award-info":[{"award-number":["1916251"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA241699","CA196530"],"award-info":[{"award-number":["CA241699","CA196530"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yale Cancer Center Pilot Award"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Heterogeneity is a hallmark of many complex human diseases, and unsupervised heterogeneity analysis has been extensively conducted using high-throughput molecular measurements and histopathological imaging features. \u2018Classic\u2019 heterogeneity analysis has been based on simple statistics such as mean, variance and correlation. Network-based analysis takes interconnections as well as individual variable properties into consideration and can be more informative. Several Gaussian graphical model (GGM)-based heterogeneity analysis techniques have been developed, but friendly and portable software is still lacking. To facilitate more extensive usage, we develop the R package HeteroGGM, which conducts GGM-based heterogeneity analysis using the advanced penaliztaion techniques, can provide informative summary and graphical presentation, and is efficient and friendly.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availabilityand implementation<\/jats:title>\n                  <jats:p>The package is available at https:\/\/CRAN.R-project.org\/package=HeteroGGM.<\/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\/btab134","type":"journal-article","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T20:13:21Z","timestamp":1614197601000},"page":"3073-3074","source":"Crossref","is-referenced-by-count":5,"title":["HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis"],"prefix":"10.1093","volume":"37","author":[{"given":"Mingyang","family":"Ren","sequence":"first","affiliation":[{"name":"School of Mathematics Sciences, University of Chinese Academy of Sciences , Beijing 100049, China"},{"name":"Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences , Beijing 100190, China"}]},{"given":"Sanguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics Sciences, University of Chinese Academy of Sciences , Beijing 100049, China"},{"name":"Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences , Beijing 100190, China"}]},{"given":"Qingzhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Econometrics, Department of Statistics, School of Economics, The Wang Yanan Institute for Studies in Economics, and Fujian Key Lab of Statistics, Xiamen University , Xiamen 361005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9001-4999","authenticated-orcid":false,"given":"Shuangge","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health , New Haven, CT 06520, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,2,26]]},"reference":[{"key":"2023061402422257900_btab134-B1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1111\/rssb.12033","article-title":"The joint graphical lasso for inverse covariance estimation across multiple classes","volume":"76","author":"Danaher","year":"2014","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol"},{"key":"2023061402422257900_btab134-B2","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1214\/16-EJS1135","article-title":"Estimation of multiple networks in Gaussian mixture models","volume":"10","author":"Gao","year":"2016","journal-title":"Electron. J. Stat"},{"key":"2023061402422257900_btab134-B3","first-page":"7981","article-title":"Simultaneous clustering and estimation of heterogeneous graphical models","volume":"18","author":"Hao","year":"2018","journal-title":"J. Mach. Learn. Res"},{"key":"2023061402422257900_btab134-B4","article-title":"Gaussian graphical model-based heterogeneity analysis via penalized fusion","author":"Ren","year":"2021","journal-title":"Biometrics"},{"key":"2023061402422257900_btab134-B5","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1214\/09-EJS487","article-title":"Penalized model-based clustering with unconstrained covariance matrices","volume":"3","author":"Zhou","year":"2009","journal-title":"Electron. J. 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