We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m mismatches, and do not rely on any generative model for the positive training sequences. We compute the kernels efficiently using a mismatch tree data structure and report experiments on a benchmark SCOP dataset, where we show that the mismatch kernel used with an SVM classifier performs as well as the Fisher kernel, the most successful method for remote homology detection, while achieving considerable computational savings.
Mismatch string kernels for SVM protein classification
Leslie, Christina, Eleazar Eskin, Adiel Cohen, Jason Weston and William Stafford Noble
Neural Information Processing Systems 15, 2003. pp. 1441-1448.
Mismatch kernel code