A better gap penalty for Pairwise SVM

Abstract

SVM-Pairwise was a major breakthrough in remote homology detection techniques, significantly outperforming previous approaches. This approach has been extensively evaluated and cited by later works, and is frequently taken as a benchmark. No known work however, has examined the gap penalty model employed by SVM-Pairwise. In this paper, we study in depth the relevance and effectiveness of SVM-Pairwise's gap penalty model with respect to the homology detection task. We have identified some limitations in this model that prevented the SVM-Pairwise algorithm from realizing its full potential and also studied several ways to overcome them. We discovered a more appropriate gap penalty model that significantly improves the performance of SVM-Pairwise.