A probabilistic framework for mismatch and profile string kernels

There has recently been numerous applications of kernel methods in the field of bioinformatics. In particular, the problem of protein homology has served as a benchmark for the performance of many new kernels which operate directly on strings (such as amino-acid sequences). Several new kernels have been developed and successfully applied to this type of data, including spectrum, string, mismatch, and profile kernels. In this paper we introduce a general probabilistic framework for stringtype kernels which uses the fisher-kernel approach and includes spectrum, mismatch and profile kernels, among others, as special cases. The use of a probabilistic model however provides additional flexibility both in definition and for the re-weighting of features through feature selection methods, prior knowledge or semi-supervised approaches which use data repositories such as BLAST. We give details of the framework, place wellknown kernels in the framework and give preliminary experimental results which show some effects of using the probabilistic approach.