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  Semi-supervised protein classification using cluster kernels

Weston, J., Leslie, C., Ie, E., Zhou, D., Elisseeff, A., & Noble, W. (2005). Semi-supervised protein classification using cluster kernels. Bioinformatics, 21(15), 3241-3247. doi:10.1093/bioinformatics/bti497.

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Weston, J, Author              
Leslie, CS, Author
Ie, E, Author
Zhou, D1, 2, Author              
Elisseeff, A1, 2, Author              
Noble, WS, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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—whereas in practice, unlabeled data are 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 and at the same time achieving far greater computationalefficiency.

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 Dates: 2005-08
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 3956
DOI: 10.1093/bioinformatics/bti497
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 21 (15) Sequence Number: - Start / End Page: 3241 - 3247 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991