Marginalized kernels for biological sequences.
BACKGROUND
Kernel methods such as support vector machines require a kernel function between objects to be defined a priori. Several works have been done to derive kernels from probability distributions, e.g., the Fisher kernel. However, a general methodology to design a kernel is not fully developed.


RESULTS
We propose a reasonable way of designing a kernel when objects are generated from latent variable models (e.g., HMM). First of all, a joint kernel is designed for complete data which include both visible and hidden variables. Then a marginalized kernel for visible data is obtained by taking the expectation with respect to hidden variables. We will show that the Fisher kernel is a special case of marginalized kernels, which gives another viewpoint to the Fisher kernel theory. Although our approach can be applied to any object, we particularly derive several marginalized kernels useful for biological sequences (e.g., DNA and proteins). The effectiveness of marginalized kernels is illustrated in the task of classifying bacterial gyrase subunit B (gyrB) amino acid sequences.
Tsuda
Koji
K
Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6 Aomi Koto-ku, Tokyo 135-0064, Japan. koji.tsuda@aist.go.jp
Kin
Taishin
T
Asai
Kiyoshi
K
D000465:Algorithms; D000595:Amino Acid Sequence; D027081:DNA Gyrase / Q000737:chemistry*; D008390:Markov Chains; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D017421:Sequence Analysis / Q000379:methods*; D017385:Sequence Homology
D027081:DNA Gyrase