{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T08:54:29Z","timestamp":1759481669508},"reference-count":10,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Summary: vbmp is an R package for Gaussian Process classification of data over multiple classes. It features multinomial probit regression with Gaussian Process priors and estimates class posterior probabilities employing fast variational approximations to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. Being equipped with only one main function and reasonable default values for optional parameters, vbmp combines flexibility with ease of usage as is demonstrated on a breast cancer microarray study.<\/jats:p><jats:p>Availability: The R library vbmp implementing this method is part of Bioconductor and can be downloaded from http:\/\/www.dcs.gla.ac.uk\/~girolami<\/jats:p><jats:p>Contact: \u00a0nicola.lama@unina2.it<\/jats:p><jats:p>Supplementary information: Supplementary data are available at http:\/\/www.dcs.gla.ac.uk\/~girolami<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm535","type":"journal-article","created":{"date-parts":[[2007,11,15]],"date-time":"2007-11-15T01:25:39Z","timestamp":1195089939000},"page":"135-136","source":"Crossref","is-referenced-by-count":6,"title":["vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R"],"prefix":"10.1093","volume":"24","author":[{"given":"Nicola","family":"Lama","sequence":"first","affiliation":[{"name":"1 Medical Statistics Unit, Department of Medicine and Public Health, Second University of Napoli, Italy and 2Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, Scotland, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Girolami","sequence":"additional","affiliation":[{"name":"1 Medical Statistics Unit, Department of Medicine and Public Health, Second University of Napoli, Italy and 2Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, Scotland, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2007,11,14]]},"reference":[{"key":"2023020209444128200_B1","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1093\/bioinformatics\/17.4.349","article-title":"Multi-class protein fold recognition using support vector machines and neural networks","volume":"17","author":"Ding","year":"2001","journal-title":"Bioinformatics"},{"key":"2023020209444128200_B2","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1162\/neco.2006.18.8.1790","article-title":"Variational Bayesian multinomial probit regression with Gaussian process priors","volume":"18","author":"Girolami","year":"2006","journal-title":"Neural Comput"},{"key":"2023020209444128200_B3","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7503.003.0063","article-title":"Data integration for classification problems employing Gaussian process priors","volume-title":"Advances in Neural Information Processing Systems.","author":"Girolami","year":"2007"},{"key":"2023020209444128200_B4","doi-asserted-by":"crossref","first-page":"3896","DOI":"10.1158\/1078-0432.CCR-05-2805","article-title":"Accurate prediction of BRCA1 and BRCA2 heterozygous genotype using expression profiling after induced DNA damage","volume":"12","author":"Kote-Jarai","year":"2006","journal-title":"Clin. 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