Support Vector Machines with Profile-Based Kernels for Remote Protein Homology Detection

Steven Busuttil[1] (steven@cs.rhul.ac.uk)
John Abela[2] (jabel@cs.um.edu.mt)
Gordon J. Pace[2] (gordon.pace@um.edu.mt)

[1]Department of Computer Science, Royal Holloway, University of London, UK
[2]Department of Computer Science and Artificial Intelligence, University of Malta, Malta

Abstract

Two new techniques for remote protein homology detection particulary suited for sparse data are introduced. These methods are based on position specific scoring matrices or profiles and use a support vector machine (SVM) for discrimination. The performance on standard benchmarks outperforms previous non-discriminative techniques and is comparable to that of other SVM-based methods while giving distinct advantages.

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