{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:11:28Z","timestamp":1761862288227},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2003,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem.<\/jats:p>\n               <jats:p>Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure.<\/jats:p>\n               <jats:p>Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 \u00b1 0.26% with a segment overlap (Sov) score of 73.32 \u00b1 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods.<\/jats:p>\n               <jats:p>Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btg223","type":"journal-article","created":{"date-parts":[[2003,9,10]],"date-time":"2003-09-10T21:55:53Z","timestamp":1063230953000},"page":"1650-1655","source":"Crossref","is-referenced-by-count":191,"title":["Secondary structure prediction with support vector machines"],"prefix":"10.1093","volume":"19","author":[{"given":"J. J.","family":"Ward","sequence":"first","affiliation":[]},{"given":"L. J.","family":"McGuffin","sequence":"additional","affiliation":[]},{"given":"B. F.","family":"Buxton","sequence":"additional","affiliation":[]},{"given":"D. T.","family":"Jones","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2003,9,1]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/19\/13\/1650\/48930074\/bioinformatics_19_13_1650.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/19\/13\/1650\/48930074\/bioinformatics_19_13_1650.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T12:04:51Z","timestamp":1674821091000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/19\/13\/1650\/225083"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,9,1]]},"references-count":0,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2003,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btg223","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2003,9,1]]},"published":{"date-parts":[[2003,9,1]]}}}