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  Protein homology detection using string alignment kernels

Saigo, H., Vert, J.-P., Ueda, N., & Akutsu, T. (2004). Protein homology detection using string alignment kernels. Bioinformatics, 20(11), 1682-1689. doi:10.1093/bioinformatics/bth141.

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Saigo, H1, Author              
Vert , J-P, Author
Ueda, N, Author
Akutsu, T, Author
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1External Organizations, ou_persistent22              

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 Abstract: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVM) are currently the most effective methods for the problem of superfamily recognition in the SCOP database. The performance of SVMs depend critically on the kernel function used to quantify the similarity between sequences. We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the art methods for remote homology detection.

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 Dates: 2004-07
 Publication Status: Published in print
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 Identifiers: DOI: 10.1093/bioinformatics/bth141
BibTex Citekey: 4102
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 20 (11) Sequence Number: - Start / End Page: 1682 - 1689 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991