Protein homology detection using string alignment kernels | Zendy

Hiroto Saigo | Zendy; JeanPhilippe Vert | Zendy; Nobuhisa Ueda | Zendy; Tatsuya Akutsu | Zendy

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Protein homology detection using string alignment kernels

Author(s) -

Hiroto Saigo,

JeanPhilippe Vert,

Nobuhisa Ueda,

Tatsuya Akutsu

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth141

Subject(s) - discriminative model , support vector machine , smith–waterman algorithm , homology (biology) , computer science , kernel (algebra) , pattern recognition (psychology) , benchmark (surveying) , artificial intelligence , similarity (geometry) , string kernel , string (physics) , sequence alignment , software , similarity measure , machine learning , computational biology , kernel method , mathematics , biology , peptide sequence , radial basis function kernel , genetics , amino acid , mathematical physics , geodesy , combinatorics , image (mathematics) , gene , programming language , geography

Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences.

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