Locality Kernels for Protein Classification
- Cite this paper as:
- Tsivtsivadze E., Boberg J., Salakoski T. (2007) Locality Kernels for Protein Classification. In: Giancarlo R., Hannenhalli S. (eds) Algorithms in Bioinformatics. WABI 2007. Lecture Notes in Computer Science, vol 4645. Springer, Berlin, Heidelberg
We propose kernels that take advantage of local correlations in sequential data and present their application to the protein classification problem. Our locality kernels measure protein sequence similarities within a small window constructed around matching amino acids. The kernels incorporate positional information of the amino acids inside the window and allow a range of position dependent similarity evaluations. We use these kernels with regularized least-squares algorithm (RLS) for protein classification on the SCOP database. Our experiments demonstrate that the locality kernels perform significantly better than the spectrum and the mismatch kernels. When used together with RLS, performance of the locality kernels is comparable with some state-of-the-art methods of protein classification and remote homology detection.
Unable to display preview. Download preview PDF.