Building multiclass classifiers for remote homology detection and fold recognition

Motivation 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. Methods We developed a number of methods for building SVMbased multiclass classification schemes in the context of the 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 st...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Huzefa Rangwala, George Karypis
• Keywords: BMCBI 2006, Homology Prediction, Problems, SCOP Hierarchy