Comparison of one-class SVM and two-class SVM for fold recognition

The best protein structure prediction results today are achieved by incorporating initial structural prediction using alignments to known protein structures. The performance of these algorithms directly depends on the quality and significance of the alignment results. Support Vector Machines (SVMs) have shown great potential in providing good alignment results in cases where very low similarities to known proteins exist. In this paper we propose the use of a one-class SVM to reduce the computational resources required to perform SVM learning and classification. Experimental results show its efficiency compared to two-class SVM algorithms while producing results of similar accuracy.