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  Mismatch string kernels for discriminative protein classification

Leslie, C., Eskin, E., Cohen, A., Weston, J., & Noble, W. (2004). Mismatch string kernels for discriminative protein classification. Bioinformatics, 20(4), 467-476. doi:10.1093/bioinformatics/btg431.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-4F63-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-4F64-3
Genre: Journal Article

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 Creators:
Leslie, CS, Author
Eskin, E, Author
Cohen, A, Author
Weston, J1, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine learning approaches provide good performance, but simplicity and computational efficiency of training and prediction are also important concerns. Results: We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the problem of protein classification and remote homology detection. These kernels measure sequence similarity based on shared occurrences of fixed-length patterns in the data, allowing for mutations between patterns. Thus, the kernels provide a biologically well-motivated way to compare protein sequences without relying on family-based generative models such as hidden Markov models. We compute the kernels efficiently using a mismatch tree data structure, allowing us to calculate the contributions of all patterns occurring in the data in one pass while traversing the tree. When used with an SVM, the kernels enable fast prediction on test sequences. We report experiments on two benchmark SCOP datasets, where we show that the mismatch kernel used with an SVM classifier performs competitively with state-of-the-art methods for homology detection, particularly when very few training examples are available. Examination of the highest-weighted patterns learned by the SVM classifier recovers biologically important motifs in protein families and superfamilies.

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 Dates: 2004-03
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btg431
 Degree: -

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 20 (4) Sequence Number: - Start / End Page: 467 - 476 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991