Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition
MINNESOTA UNIV MINNEAPOLIS DEPT OF COMPUTER SCIENCE
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Protein remote homology prediction and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problem. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems. We developed a number of methods for building SVM based multiclass classification schemes in the context of SCOP protein classification. These methods includes schemes that directly build an SVM-based multiclass model, schemes that employ a second level learning approach to combine the predictions generated by a set of binary SVM-based classifiers, and schemes that build and combine binary classifiers for various levels of the SCOP hierarchy beyond those defining the target classes. Results We performed a comprehensive study analyzing the different approaches using four different datasets. Our results show that most of the proposed multiclass SVM-based classification approaches are quite effective in solving the remote homology prediction and fold recognition problems and that the schemes that use predictions from binary models constructed for ancestral categories within the SCOP hierarchy tend to qualitatively improve the prediction results.
- Numerical Mathematics