{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T12:13:02Z","timestamp":1771071182026,"version":"3.50.1"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top five methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified.<\/jats:p>\n               <jats:p>Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.<\/jats:p>\n               <jats:p>Availability: \u00a0http:\/\/www.reading.ac.uk\/bioinf\/DISOclust\/<\/jats:p>\n               <jats:p>Contact: \u00a0l.j.mcguf.n@reading.ac.uk<\/jats:p>\n               <jats:p>Supplementary information: Supplementary data are available at http:\/\/www.reading.ac.uk\/bioinf\/DISOclust\/suppl.pdf<\/jats:p>","DOI":"10.1093\/bioinformatics\/btn326","type":"journal-article","created":{"date-parts":[[2008,6,26]],"date-time":"2008-06-26T00:44:20Z","timestamp":1214441060000},"page":"1798-1804","source":"Crossref","is-referenced-by-count":105,"title":["Intrinsic disorder prediction from the analysis of multiple protein fold recognition models"],"prefix":"10.1093","volume":"24","author":[{"given":"Liam J.","family":"McGuffin","sequence":"first","affiliation":[{"name":"School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2008,6,25]]},"reference":[{"key":"2023020210500849900_B1","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1002\/prot.21671","article-title":"Assessment of disorder predictions in CASP7","volume":"69","author":"Bordoli","year":"2007","journal-title":"Proteins"},{"key":"2023020210500849900_B2","first-page":"213","article-title":"Accurate prediction of protein disordered regions by mining protein structure data","volume-title":"Data Mining and Knowledge Discovery.","author":"Cheng","year":"2005"},{"key":"2023020210500849900_B3","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1002\/prot.21669","article-title":"Assessment of predictions in the model quality assessment category","volume":"69","author":"Cozzetto","year":"2007","journal-title":"Proteins"},{"key":"2023020210500849900_B4","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1002\/prot.10357","article-title":"3D-SHOTGUN: a novel, cooperative, fold-recognition metapredictor","volume":"51","author":"Fischer","year":"2003","journal-title":"Proteins"},{"key":"2023020210500849900_B5","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1093\/bioinformatics\/btg124","article-title":"3D-Jury: a simple approach to improve protein structure predictions","volume":"19","author":"Ginalski","year":"2003","journal-title":"Bioinformatics"},{"key":"2023020210500849900_B6","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"2023020210500849900_B7","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1148\/radiology.148.3.6878708","article-title":"A method of comparing the areas under receiver operating characteristic curves derived from the same cases","volume":"148","author":"Hanley","year":"1983","journal-title":"Radiology"},{"key":"2023020210500849900_B8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1002\/prot.20734","article-title":"Assessment of disorder predictions in CASP6","volume":"61","author":"Jin","year":"2005","journal-title":"Proteins"},{"key":"2023020210500849900_B9","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/prot.20731","article-title":"Prediction of novel and analogous folds using fragment assembly and fold recognition","volume":"61","author":"Jones","year":"2005","journal-title":"Proteins"},{"key":"2023020210500849900_B10","doi-asserted-by":"crossref","first-page":"5913","DOI":"10.1073\/pnas.95.11.5913","article-title":"A unified statistical framework for sequence comparison and structure comparison","volume":"95","author":"Levitt","year":"1998","journal-title":"Proc. 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