{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:25:21Z","timestamp":1770830721312,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"5","funder":[{"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":[[2016,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data.<\/jats:p><jats:p>Results: Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to genomic datasets from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serous cystadenocarcinoma. The integrative models lead to improved model fit and predictive performance. They also provide a better understanding of the biological mechanisms underlying patients\u2019 survival.<\/jats:p><jats:p>Availability and implementation: Source code implementing the integrative models is freely available at https:\/\/github.com\/mgt000\/IntegrativeAnalysis along with example datasets and sample R script applying the models to these data. The TCGA datasets used for analysis are publicly available at https:\/\/tcga-data.nci.nih.gov\/tcga\/tcgaDownload.jsp.<\/jats:p><jats:p>Contact: \u00a0marie.denis@cirad.fr or mgt26@georgetown.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv653","type":"journal-article","created":{"date-parts":[[2015,11,7]],"date-time":"2015-11-07T02:04:32Z","timestamp":1446861872000},"page":"738-746","source":"Crossref","is-referenced-by-count":7,"title":["Evaluation of hierarchical models for integrative genomic analyses"],"prefix":"10.1093","volume":"32","author":[{"given":"Marie","family":"Denis","sequence":"first","affiliation":[{"name":"1 UMR AGAP, CIRAD, Montpellier, France,"},{"name":"2 Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahlet G.","family":"Tadesse","sequence":"additional","affiliation":[{"name":"3 Department of Mathematics and Statistics, Georgetown University, Washington, DC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,11,5]]},"reference":[{"key":"2023020110445240200_btv653-B1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/nrg3575","article-title":"Systems genetics approaches to understand complex traits","volume":"15","author":"Civelek","year":"2014","journal-title":"Nat. Rev. Genet."},{"key":"2023020110445240200_btv653-B2","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1515\/sagmb-2012-0051","article-title":"A graphical model method for integrating multiple sources of genome-scale data","volume":"12","author":"Dvorkin","year":"2013","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"2023020110445240200_btv653-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"2023020110445240200_btv653-B4","doi-asserted-by":"crossref","first-page":"230","DOI":"10.7150\/ijbs.9193","article-title":"Efficient inhibition of human glioma development by RNA interference-mediated silencing of PAK5","volume":"12","author":"Gu","year":"2015","journal-title":"Int. J. Biol. Sci."},{"key":"2023020110445240200_btv653-B5","first-page":"1","article-title":"Data integration in genetics and genomics: methods and challenges","volume":"2009","author":"Hamid","year":"2009","journal-title":"Hum. Genomics Proteomics."},{"key":"2023020110445240200_btv653-B6","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/s10014-013-0161-1","article-title":"Downregulation of PAK5 inhibits glioma cell migration and invasion potentially through the PAK5-Egr1-MMP2 signaling pathway","volume":"31","author":"Han","year":"2015","journal-title":"Brain Tumor Pathol."},{"key":"2023020110445240200_btv653-B7","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3462-1","volume-title":"Regression Modeling Strategies, With Applications to Linear Models, Logistic Regression, and Survival Analysis","author":"Harrell","year":"2001"},{"key":"2023020110445240200_btv653-B8","first-page":"703","article-title":"Individual survival time prediction using statistical models","volume":"31","author":"Henderson","year":"2005","journal-title":"Clin. Ethics"},{"key":"2023020110445240200_btv653-B9","article-title":"Bayesian methods for expression-based integration of various types of genomics data","volume":"13","author":"Jennings","year":"2013","journal-title":"EURASIP J. Bioinf. Syst. Biol."},{"key":"2023020110445240200_btv653-B10","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1214\/09-BA416","article-title":"A stochastic partitioning method to associate high-dimensional responses and covariates (with discussion)","volume":"4","author":"Monni","year":"2009","journal-title":"Bayesian Anal."},{"key":"2023020110445240200_btv653-B11","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1038\/nature02797","article-title":"Genetic analysis of genome-wide variation in human gene expression","volume":"430","author":"Morley","year":"2004","journal-title":"Nature"},{"key":"2023020110445240200_btv653-B12","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1093\/biomet\/78.3.691","article-title":"A note on a general definition of the coefficient of determination","volume":"78","author":"Nagelkerke","year":"1991","journal-title":"Biometrika"},{"key":"2023020110445240200_btv653-B13","doi-asserted-by":"crossref","first-page":"12963","DOI":"10.1073\/pnas.162471999","article-title":"Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors","volume":"99","author":"Pollack","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020110445240200_btv653-B14","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1093\/bioinformatics\/btp543","article-title":"Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis","volume":"25","author":"Shen","year":"2009","journal-title":"Bioinformatics"},{"key":"2023020110445240200_btv653-B15","article-title":"A blockwise descent algorithm for group-penalized multiresponse and multinomial regression","author":"Simon","year":"2013","journal-title":"arXiv"},{"key":"2023020110445240200_btv653-B16","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1126\/science.1136678","article-title":"Relative impact of nucleotide and copy number variation on gene expression phenotypes","volume":"315","author":"Stranger","year":"2007","journal-title":"Science"},{"key":"2023020110445240200_btv653-B17","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1038\/nature10166","article-title":"Integrated genomic analyses of ovarian carcinoma","volume":"474","author":"The Cancer Genome Atlas Research Network","year":"2011","journal-title":"Nature"},{"key":"2023020110445240200_btv653-B18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Statist. Soc. Ser. B"},{"key":"2023020110445240200_btv653-B19","doi-asserted-by":"crossref","first-page":"R105+","DOI":"10.1186\/gb-2011-12-10-r105","article-title":"Integrating diverse genomic data using gene sets","volume":"12","author":"Tyekucheva","year":"2011","journal-title":"Genome Biol."},{"key":"2023020110445240200_btv653-B20","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1534\/genetics.110.116087","article-title":"Expression quantitative trait loci: replication, tissue- and sex-specificity in mice","volume":"185","author":"van Nas","year":"2010","journal-title":"Genetics"},{"key":"2023020110445240200_btv653-B21","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1016\/j.csda.2008.05.021","article-title":"Survival prediction using gene expression data: a review and comparison","volume":"53","author":"van Wieringen","year":"2009","journal-title":"Comput. Stat. Data Anal."},{"key":"2023020110445240200_btv653-B22","doi-asserted-by":"crossref","first-page":"R37+","DOI":"10.1186\/gb-2014-15-2-r37","article-title":"The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts","volume":"15","author":"Wagner","year":"2014","journal-title":"Genome Biol."},{"key":"2023020110445240200_btv653-B23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/1471-2407-8-79","article-title":"Increased expression of epha7 correlates with adverse outcome in primary and recurrent glioblastoma multiforme patients","volume":"8","author":"Wang","year":"2008","journal-title":"BMC Cancer"},{"key":"2023020110445240200_btv653-B24","first-page":"149","article-title":"iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data","volume":"29","author":"Wang","year":"2013","journal-title":"Bioinformatics (Oxford, England)"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/5\/738\/49017528\/bioinformatics_32_5_738.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/5\/738\/49017528\/bioinformatics_32_5_738.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T09:00:30Z","timestamp":1718182830000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/5\/738\/1744452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,5]]},"references-count":24,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2016,3,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btv653","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,3,1]]},"published":{"date-parts":[[2015,11,5]]}}}