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Title | Improved disorder prediction by combination of orthogonal approaches. |
Publication Type | Journal Article |
Year of Publication | 2009 |
Authors | Schlessinger, A, Punta, M, Yachdav, G, Kaján, L, Rost, B |
Journal | PLoS One |
Volume | 4 |
Issue | 2 |
Pagination | e4433 |
Date Published | 2009 |
ISSN | 1932-6203 |
Keywords | Computational Biology, Databases, Protein, Models, Molecular, Proteins, Reproducibility of Results |
Abstract | Disordered proteins are highly abundant in regulatory processes such as transcription and cell-signaling. Different methods have been developed to predict protein disorder often focusing on different types of disordered regions. Here, we present MD, a novel META-Disorder prediction method that molds various sources of information predominantly obtained from orthogonal prediction methods, to significantly improve in performance over its constituents. In sustained cross-validation, MD not only outperforms its origins, but it also compares favorably to other state-of-the-art prediction methods in a variety of tests that we applied. Availability: http://www.rostlab.org/services/md/ |
DOI | 10.1371/journal.pone.0004433 |
Alternate Journal | PLoS ONE |
PubMed ID | 19209228 |
PubMed Central ID | PMC2635965 |
Grant List | R01-LM07329 / LM / NLM NIH HHS / United States |