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Bioinformatics Advance Access published online on May 19, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti497
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received January 2, 2005
Revised April 26, 2005
Accepted May 12, 2005

Article

Semi-supervised protein classification using cluster kernels

Jason Weston 1*, Christina Leslie 2, Eugene Ie 2, Dengyong Zhou 3, Andre Elisseeff 3, and William Stafford Noble 4

1 NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA
2 Center for Computational Learning Systems, Columbia University, Interchuch Center, 475 Riverside Dr., Mail Code 7717, New York, NY 10115, USA
3 Max-Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076 Tübingen, Germany
4 Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA

* To whom correspondence should be addressed.
Jason Weston, E-mail: jasonw{at}nec-labs.com


  Abstract

Motivation: Building an accurate protein classification system depends critically upon choosing a good representation of the input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data--examples with known 3D structures, organized into structural classes--while in practice, unlabeled data is far more plentiful.

Results: In this work, we develop simple and scalable cluster kernel techniques for incorporating unlabeled data into the representation of protein sequences. We show that our methods greatly improve the classification performance of string kernels and outperform standard approaches for using unlabeled data, such as adding close homologs of the positive examples to the training data. We achieve equal or superior performance to previously presented cluster kernel methods while achieving far greater computational efficiency.

Availability: Supplementary data and source code are available at www.kyb.tuebingen.mpg.de/bs/people/weston/semiprot. The Spider matlab package is available at www.kyb.tuebingen.mpg.de/bs/people/spider.


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