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

@article{Hndstad2004MotifKG,
  title={Motif kernel generated by genetic programming improves remote homology and fold detection},
  author={Tony H{\aa}ndstad and Arne J. H. Hestnes and P{\aa}l S{\ae}trom},
  journal={BMC Bioinformatics},
  year={2004},
  volume={8},
  pages={23 - 23}
}
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. We introduce the GPkernel, which is… CONTINUE READING
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