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...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Tony Håndstad, Arne J. H. Hestnes, Pål Sætrom
• Keywords: BMCBI 2007, Discrete Sequence Motifs, Protein, Sequences