Title |
Improved Disorder Prediction by Combination of Orthogonal Approaches
|
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Published in |
PLOS ONE, February 2009
|
DOI | 10.1371/journal.pone.0004433 |
Pubmed ID | |
Authors |
Avner Schlessinger, Marco Punta, Guy Yachdav, Laszlo Kajan, Burkhard Rost |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 3 | 3% |
Germany | 3 | 3% |
United States | 2 | 2% |
Unknown | 100 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 29 | 27% |
Researcher | 25 | 23% |
Student > Master | 10 | 9% |
Student > Bachelor | 7 | 6% |
Professor | 7 | 6% |
Other | 18 | 17% |
Unknown | 12 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 43 | 40% |
Biochemistry, Genetics and Molecular Biology | 34 | 31% |
Chemistry | 7 | 6% |
Computer Science | 7 | 6% |
Mathematics | 2 | 2% |
Other | 4 | 4% |
Unknown | 11 | 10% |