A Class of Edit Kernels for SVMs to Predict Translation Initiation Sites in Eukaryotic mRNAs

Dr Tao Jiang
Dept of Computer Science
UC Riverside

Room 610, CBI
2:00PM, Tuesday, 6 July 2004

The prediction of translation initiation sites (TISs)
in eukaryotic mRNAs has been a challenging problem in computational molecular biology. In this paper, we present a new algorithm to recognize TISs with a very high accuracy. Our algorithm includes two novel ideas. First, we introduce a class of new sequence-similarity kernels based on string edit, called the edit kernels, for use with support vector machines
(SVMs) in a discriminative approach to predict TISs. SVMs are among the most powerful and popular machine learning techniques. The edit kernels are simple and have significant biological and probabilistic interpretations. Second, we convert the region of an input mRNA sequence downstream to a putative TIS into an amino acid sequence before applying SVMs to avoid the high redundancy in the genetic code. The algorithm has been implemented and tested on previously published data. Our experimental results on real mRNA data show that both ideas improve the prediction accuracy greatly and our method performs significantly better than those based on neural networks and SVMs with polynomial kernels or Salzberg kernel.

This is joint work with Haifeng Li.