{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:03:47Z","timestamp":1761807827739},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Gut microbiota can be classified at multiple taxonomy levels. Strategies to use changes in microbiota composition to effect health improvements require knowing at which taxonomy level interventions should be aimed. Identifying these important levels is difficult, however, because most statistical methods only consider when the microbiota are classified at one taxonomy level, not multiple.<\/jats:p><jats:p>Results: Using L1 and L2 regularizations, we developed a new variable selection method that identifies important features at multiple taxonomy levels. The regularization parameters are chosen by a new, data-adaptive, repeated cross-validation approach, which performed well. In simulation studies, our method outperformed competing methods: it more often selected significant variables, and had small false discovery rates and acceptable false-positive rates. Applying our method to gut microbiota data, we found which taxonomic levels were most altered by specific interventions or physiological status.<\/jats:p><jats:p>Availability: The new approach is implemented in an R package, which is freely available from the corresponding author.<\/jats:p><jats:p>Contact: tpgarcia@srph.tamhsc.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt608","type":"journal-article","created":{"date-parts":[[2013,10,26]],"date-time":"2013-10-26T00:23:57Z","timestamp":1382747037000},"page":"831-837","source":"Crossref","is-referenced-by-count":30,"title":["Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data"],"prefix":"10.1093","volume":"30","author":[{"given":"Tanya P.","family":"Garcia","sequence":"first","affiliation":[{"name":"1 Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, 2School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, 3Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and 4Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"1 Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, 2School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, 3Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and 4Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raymond J.","family":"Carroll","sequence":"additional","affiliation":[{"name":"1 Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, 2School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, 3Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and 4Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosemary L.","family":"Walzem","sequence":"additional","affiliation":[{"name":"1 Department of Epidemiology & Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266, USA, 2School of Mathematics and Statistics, University of Sydney, NSW 2006 Australia, 3Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA and 4Department of Poultry Science, Intercollegiate Faculty of Nutrition, Texas A&M University, College Station, TX 77840, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,10,24]]},"reference":[{"key":"2023012710444932800_btt608-B1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"JRSSB"},{"key":"2023012710444932800_btt608-B2","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1097\/SLA.0b013e318262a6a6","article-title":"Murine gut microbiota and transcriptome are diet dependent","volume":"257","author":"Carlisle","year":"2013","journal-title":"Ann. 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