Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Protein homology detection using string alignment kernels

There are no MPG-Authors in the publication available
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Saigo, H., Vert, J.-P., Ueda, N., & Akutsu, T. (2004). Protein homology detection using string alignment kernels. Bioinformatics, 20(11), 1682-1689. doi:10.1093/bioinformatics/bth141.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-F3D1-3
Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVM) are currently the most effective methods for the problem of superfamily recognition in the SCOP database. The performance of SVMs depend critically on the kernel function used to quantify the similarity between sequences. We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the art methods for remote homology detection.