{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:21:09Z","timestamp":1784031669594,"version":"3.55.0"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"24","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,12,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: One central theme of modern high-throughput genomic data analysis is to identify relevant genomic features as well as build up a predictive model based on selected features for various tasks such as personalized medicine. Correlating the large number of \u2018omics\u2019 features with a certain phenotype is particularly challenging due to small sample size (n) and high dimensionality (p). To address this small n, large p problem, various forms of sparse regression models have been proposed by exploiting the sparsity assumption. Among these, network-constrained sparse regression model is of particular interest due to its ability to utilize the prior graph\/network structure in the omics data. Despite its potential usefulness for omics data analysis, no efficient R implementation is publicly available. Here we present an R software package \u2018glmgraph\u2019 that implements the graph-constrained regularization for both sparse linear regression and sparse logistic regression. We implement both the L1 penalty and minimax concave penalty for variable selection and Laplacian penalty for coefficient smoothing. Efficient coordinate descent algorithm is used to solve the optimization problem. We demonstrate the use of the package by applying it to a human microbiome dataset, where phylogeny structure among bacterial taxa is available.<\/jats:p>\n               <jats:p>Availability and implementation: \u2018glmgraph\u2019 is implemented in R and C++ Armadillo and publicly available under CRAN.<\/jats:p>\n               <jats:p>Contact: \u00a0chen.jun2@mayo.edu or hongzhe@upenn.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv497","type":"journal-article","created":{"date-parts":[[2015,8,28]],"date-time":"2015-08-28T00:18:54Z","timestamp":1440721134000},"page":"3991-3993","source":"Crossref","is-referenced-by-count":29,"title":["glmgraph: an R package for variable selection and predictive modeling of structured genomic data"],"prefix":"10.1093","volume":"31","author":[{"given":"Li","family":"Chen","sequence":"first","affiliation":[{"name":"1 Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905,USA,"},{"name":"2 Department of Computer Science, Emory University, Atlanta, GA 30322,USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"3 Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Pierre A.","family":"Kocher","sequence":"additional","affiliation":[{"name":"1 Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905,USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongzhe","family":"Li","sequence":"additional","affiliation":[{"name":"4 Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"1 Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905,USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,8,26]]},"reference":[{"key":"2023051307184950400_btv497-B1","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1214\/10-AOAS388","article-title":"Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection","volume":"5","author":"Breheny","year":"2011","journal-title":"Ann. Appl. Stat."},{"key":"2023051307184950400_btv497-B2","doi-asserted-by":"crossref","first-page":"e15216","DOI":"10.1371\/journal.pone.0015216","article-title":"Disordered microbial communities in the upper respiratory tract of cigarette smokers","volume":"5","author":"Charlson","year":"2010","journal-title":"PLos One"},{"key":"2023051307184950400_btv497-B3","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1093\/biostatistics\/kxs038","article-title":"Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis","volume":"14","author":"Chen","year":"2013","journal-title":"Biostatistics"},{"key":"2023051307184950400_btv497-B4","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":"2023051307184950400_btv497-B5","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1214\/11-AOS897","article-title":"The sparse laplacian shrinkage estimator for high-dimensional regression","volume":"39","author":"Huang","year":"2011","journal-title":"Ann. Stat."},{"key":"2023051307184950400_btv497-B6","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1093\/bioinformatics\/btn081","article-title":"Network-constrained regularization and variable selection for analysis of genomic data","volume":"24","author":"Li","year":"2008","journal-title":"Bioinformatics"},{"key":"2023051307184950400_btv497-B7","first-page":"25","article-title":"Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction","volume":"13","author":"Tian","year":"2014","journal-title":"Cancer Inf."},{"key":"2023051307184950400_btv497-B8","doi-asserted-by":"crossref","first-page":"3399","DOI":"10.1093\/bioinformatics\/btr591","article-title":"Optimized application of penalized regression methods to diverse genomic data","volume":"27","author":"Waldron","year":"2011","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/24\/3991\/50307014\/bioinformatics_31_24_3991.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/24\/3991\/50307014\/bioinformatics_31_24_3991.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T07:20:05Z","timestamp":1683962405000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/31\/24\/3991\/197681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8,26]]},"references-count":8,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2015,12,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btv497","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2015,12,15]]},"published":{"date-parts":[[2015,8,26]]}}}