Learned Random-Walk Kernels and Empirical-Map Kernels for Protein Sequence Classification

Abstract

Biological sequence classification (such as protein remote homology detection) solely based on sequence data is an important problem in computational biology, especially in the current genomics era, when large amount of sequence data are becoming available. Support vector machines (SVMs) based on mismatch string kernels were previously applied to solve this problem, achieving reasonable success. However, they still perform poorly on difficult protein families. In this paper, we propose two approaches to solve the protein remote homology detection problem: one uses a convex combination of random-walk kernels to approximate the random-walk kernel with the optimal random steps, and the other constructs an empirical-map kernel using a profile kernel. Both resulting kernels make use of a large number of pairwise sequence similarity information and unlabeled data; and have much better prediction performance than the best profile kernel directly derived from protein sequences. On a competitive Structural Classification Of Proteins (SCOP) benchmark dataset, the overall mean ROC(50) scores on 54 protein families we obtained using both approaches are above 0.90, which significantly outperform previous published results.