{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T03:31:34Z","timestamp":1775705494802,"version":"3.50.1"},"reference-count":7,"publisher":"Oxford University Press (OUP)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.<\/jats:p>\n               <jats:p>Availability: GALGO can be downloaded from<\/jats:p>\n               <jats:p>Contact: \u00a0vtrevino@itesm.mx; f.falciani@bham.ac.uk<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl074","type":"journal-article","created":{"date-parts":[[2006,3,2]],"date-time":"2006-03-02T01:24:53Z","timestamp":1141262693000},"page":"1154-1156","source":"Crossref","is-referenced-by-count":126,"title":["GALGO: an R package for multivariate variable selection using genetic algorithms"],"prefix":"10.1093","volume":"22","author":[{"given":"Victor","family":"Trevino","sequence":"first","affiliation":[{"name":"School of Biosciences, University of Birmingham \u00a0 Birmingham, B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Falciani","sequence":"additional","affiliation":[{"name":"School of Biosciences, University of Birmingham \u00a0 Birmingham, B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2006,3,1]]},"reference":[{"key":"2023012409133611200_b1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1201\/9780203011232.ch3","article-title":"Classification in microarray experiments","volume-title":"Statistical Analysis of Gene Expression Microarray Data","author":"Dudoit","year":"2003"},{"key":"2023012409133611200_b2","doi-asserted-by":"crossref","first-page":"R80","DOI":"10.1186\/gb-2004-5-10-r80","article-title":"Bioconductor: open software development for computational biology and bioinformatics","volume":"5","author":"Gentleman","year":"2004","journal-title":"Genome Biol."},{"key":"2023012409133611200_b3","first-page":"412","volume-title":"Genetic Algorithms in Search, Optimization, and Machine Learning","author":"Goldberg","year":"1989"},{"key":"2023012409133611200_b4","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.csda.2004.03.017","article-title":"An extensive comparison of recent classification tools applied to microarray data","volume":"48","author":"Lee","year":"2005","journal-title":"Comput. 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