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BMCBI
2007

Motif kernel generated by genetic programming improves remote homology and fold detection

9 years 3 months ago
Motif kernel generated by genetic programming improves remote homology and fold detection
Background: Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences. Results: We introduce the GPkernel, which is a motif kernel based on discrete sequence motifs where the motifs are evolved using genetic programming. All proteins can be grouped according to evolutionary relations and structure, and the method uses this inherent structure to create groups of motifs that discriminate between different families of evolutionary origin. When tested on two SCOP benchmarks, the superfamily and fold recognition problems, the GPkernel gives significantly better results compared to related met...
Tony Håndstad, Arne J. H. Hestnes, Pål
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Tony Håndstad, Arne J. H. Hestnes, Pål Sætrom
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