Word correlation matrices for protein sequence analysis and remote homology detection

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. Conclusion: Our word correlation approach provides highly competitive performance as compared with state-of-the-art methods for protein remote homology detection. The learned model is interpretable in terms of biolo...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Thomas Lingner, Peter Meinicke
• Keywords: BMCBI 2008, Protein Remote Homology, Protein Sequences, Sequences