A Novel Kernel Method for RNA Sequence Data Analysis 1 Marginalized Kernels for RNA Sequence Data Analysis
We present novel kernels that measure similarity of two RNA s equences, taking account of their secondary structures. Two types of kernels are presented. One i s for RNA sequences with-, the other for those without known secondary structures. The latter emplo ys stochastic context-free grammar (SCFG) for estimating the secondary structure. We call the latter t h marginalized count kernel (MCK). We show computational experiments for MCK using 74 sets of human tRN A sequence data: (i) kernel principal component analysis (PCA) for visualizing tRNA similarities, ( ii) supervised classification with support vector machines (SVMs). Both types of experiment show promising re sults for MCKs.