Open AccessProfile-based direct kernels for remote homology detection and fold recognition
Author(s) -
Huzefa Rangwala,
George Karypis
Publication year - 2005
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/bti687
Subject(s) - support vector machine , homology (biology) , kernel (algebra) , computer science , pattern recognition (psychology) , similarity (geometry) , structural classification of proteins database , kernel method , artificial intelligence , sequence homology , smith–waterman algorithm , persistent homology , sequence alignment , algorithm , mathematics , protein structure , biology , peptide sequence , genetics , combinatorics , image (mathematics) , biochemistry , gene
Protein remote homology detection is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them.
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