Generative Kernels for Gene Function Prediction Through Probabilistic Tree Models of Evolution

Abstract

In this paper we extend kernel functions defined on generative models to embed phylogenetic information into a discriminative learning approach. We describe three generative tree kernels, a Fisher kernel, a sufficient statistics kernel and a probability product kernel, whose key features are the adaptivity to the input domain and the ability to deal with structured data. In particular, kernel adaptivity is obtained through the estimation of a tree structured model of evolution starting from the phylogenetic profiles encoding the presence or absence of specific proteins in a set of fully sequenced genomes. We report preliminary results obtained by these kernels in the prediction of the functional class of the proteins of S. Cervisae, together with comparisons to a standard vector based kernel and to a non-adaptive tree kernel function.