{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T13:58:05Z","timestamp":1761487085325},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: With the emerging success of protein secondary structure prediction through the applications of various statistical and machine learning techniques, similar techniques have been applied to protein \u03b2-turn prediction. In this study, we perform protein \u03b2-turn prediction using a k-nearest neighbor method, which is combined with a filter that uses predicted protein secondary structure information. Traditional \u03b2-turn prediction from k-nearest neighbor method is modified to account for the unbalanced ratio of the natural occurrence of \u03b2-turns and non-\u03b2-turns.<\/jats:p>\n               <jats:p>Results: Our prediction scheme is tested on a set of 426 non-homologous protein sequences. The prediction scheme consists of two stages: k-nearest neighbor method stage and filtering stage. Variations of the k-nearest neighbor method were used to take property of \u03b2-turns into consideration. Our filtering method uses \u03b2-turn\/non-\u03b2-turn estimates from the k-nearest neighbor method stage and predicted protein secondary structure information from PSI-PRED in order to get new \u03b2-turn\/non-\u03b2-turn estimate. Our result is compared with the previously best known \u03b2-turn prediction method on the dataset of 426 non-homologous protein sequences and is shown to give slightly superior performance at significantly lower computational complexity.<\/jats:p>\n               <jats:p>Availability: Contact the author for information on the source code of the programs used.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btg368","type":"journal-article","created":{"date-parts":[[2003,12,23]],"date-time":"2003-12-23T16:57:44Z","timestamp":1072198664000},"page":"40-44","source":"Crossref","is-referenced-by-count":36,"title":["Protein \u03b2-turn prediction using nearest-neighbor method"],"prefix":"10.1093","volume":"20","author":[{"given":"Saejoon","family":"Kim","sequence":"first","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2004,1,1]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/1\/40\/48905177\/bioinformatics_20_1_40.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/1\/40\/48905177\/bioinformatics_20_1_40.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T18:47:54Z","timestamp":1674672474000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/20\/1\/40\/228806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,1,1]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2004,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btg368","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2004,1,1]]},"published":{"date-parts":[[2004,1,1]]}}}