Word correlation matrices for protein sequence analysis and remote homology detection.

ABSTRACT:

Background

Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive.

Results

In this work we present a novel kernel for protein sequences based on average word similarity between two sequences. We show that this kernel gives rise to a feature space that allows analysis of discriminative features and fast classification of new sequences. We demonstrate the performance of our approach on a widely-used benchmark setup for protein remote homology detection.

SUBMITTER: Lingner T 

PROVIDER: S-EPMC2438326 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature