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Bioinformatics Advance Access originally published online on September 27, 2005
Bioinformatics 2005 21(23):4239-4247; doi:10.1093/bioinformatics/bti687
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

Profile-based direct kernels for remote homology detection and fold recognition

Huzefa Rangwala and George Karypis *

Department of Computer Science and Engineering, University of Minnesota Minneapolis, MN 55455, USA

*To whom correspondence should be addressed.

Motivation: Protein remote homology detection is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them.

Results: We introduce two classes of kernel functions that are constructed by combining sequence profiles with new and existing approaches for determining the similarity between pairs of protein sequences. These kernels are constructed directly from these explicit protein similarity measures and employ effective profile-to-profile scoring schemes for measuring the similarity between pairs of proteins. Experiments with remote homology detection and fold recognition problems show that these kernels are capable of producing results that are substantially better than those produced by all of the existing state-of-the-art SVM-based methods. In addition, the experiments show that these kernels, even when used in the absence of profiles, produce results that are better than those produced by existing non-profile-based schemes.

Availability: The programs for computing the various kernel functions are available on request from the authors.

Contact: karypis{at}cs.umn.edu

Received on May 12, 2005; revised on June 15, 2005; accepted on September 20, 2005

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