{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:13:36Z","timestamp":1778663616750,"version":"3.51.4"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"24","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,12,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Protein residue\u2013residue contact prediction can be useful in predicting protein 3D structures. Current algorithms for such a purpose leave room for improvement.<\/jats:p>\n               <jats:p>Results: We develop ProC_S3, a set of Random Forest algorithm-based models, for predicting residue\u2013residue contact maps. The models are constructed based on a collection of 1490 non\u2013redundant, high-resolution protein structures using &amp;gt;1280 sequence-based features. A new amino acid residue contact propensity matrix and a new set of seven amino acid groups based on contact preference are developed and used in ProC_S3. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L\/5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation \u226524). Further benchmark tests deliver an accuracy of 29.7% and coverage of 5.6% for an independent set of 329 proteins. In the recently completed Ninth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), ProC_S3 is ranked as No. 1, No. 3, and No. 2 accuracies in the top L\/5, L\/10 and best 5 predictions of long-range contacts, respectively, among 18 automatic prediction servers.<\/jats:p>\n               <jats:p>Availability: \u00a0http:\/\/www.abl.ku.edu\/proc\/proc_s3.html.<\/jats:p>\n               <jats:p>Contact: \u00a0jwfang@ku.edu<\/jats:p>\n               <jats:p>Supplementary Information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr579","type":"journal-article","created":{"date-parts":[[2011,10,21]],"date-time":"2011-10-21T02:07:11Z","timestamp":1319162831000},"page":"3379-3384","source":"Crossref","is-referenced-by-count":50,"title":["Predicting residue\u2013residue contacts using random forest models"],"prefix":"10.1093","volume":"27","author":[{"given":"Yunqi","family":"Li","sequence":"first","affiliation":[{"name":"Applied Bioinformatics Laboratory, The University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaping","family":"Fang","sequence":"additional","affiliation":[{"name":"Applied Bioinformatics Laboratory, The University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwen","family":"Fang","sequence":"additional","affiliation":[{"name":"Applied Bioinformatics Laboratory, The University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2011,10,20]]},"reference":[{"key":"2023012511310096100_B1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J. 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