Combining classifiers for improved classification of proteins from sequence or structure |
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| Authors: | Iain Melvin Jason Weston Christina S Leslie William S Noble |
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| Affiliation: | (1) NEC Laboratories of America, Princeton, NJ, USA;(2) Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, NY, USA;(3) Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA |
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| Abstract: | Background Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage. |
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