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The determination of coreceptor usage of HIV-1, from its protein envelope sequence, falls into a well-studied machine learning problem known as<jats:italic>classification<\/jats:italic>. The support vector machine (SVM), with string kernels, has proven to be very efficient for dealing with a wide class of classification problems ranging from text categorization to protein homology detection. In this paper, we investigate how the SVM can predict HIV-1 coreceptor usage when it is equipped with an appropriate string kernel.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Three string kernels were compared. Accuracies of 96.35% (CCR5) 94.80% (CXCR4) and 95.15% (CCR5 and CXCR4) were achieved with the SVM equipped with the<jats:italic>distant segments kernel<\/jats:italic>on a test set of 1425 examples with a classifier built on a training set of 1425 examples. Our datasets are built with Los Alamos National Laboratory HIV Databases sequences. A web server is available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/genome.ulaval.ca\/hiv-dskernel\" ext-link-type=\"uri\">http:\/\/genome.ulaval.ca\/hiv-dskernel<\/jats:ext-link>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We examined string kernels that have been used successfully for protein homology detection and propose a new one that we call the<jats:italic>distant segments kernel<\/jats:italic>. We also show how to extract the most relevant features for HIV-1 coreceptor usage. The SVM with the<jats:italic>distant segments kernel<\/jats:italic>is currently the best method described.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1742-4690-5-110","type":"journal-article","created":{"date-parts":[[2008,12,4]],"date-time":"2008-12-04T19:17:12Z","timestamp":1228418232000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels"],"prefix":"10.1186","volume":"5","author":[{"given":"S\u00e9bastien","family":"Boisvert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Marchand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fran\u00e7ois","family":"Laviolette","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacques","family":"Corbeil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2008,12,4]]},"reference":[{"key":"935_CR1","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1089\/088922203762688658","volume":"19","author":"S Pillai","year":"2003","unstructured":"Pillai S, Good B, Richman D, Corbeil J: A new perspective on V3 phenotype prediction. 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