PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exploiting Physico-Chemical Properties in String Kernels
Nora C Toussaint, Christian Widmer, Oliver Kohlbacher and Gunnar Raetsch
BMC Bioinformatics 2010 Volume 11, Number Suppl 8, S7, 2010.


Background String kernels are commonly used for the classification of biological sequences, nucleotide as well as amino acid sequences. Although string kernels are already very powerful, when it comes to amino acids they have a major short coming. They ignore an important piece of information when comparing amino acids: the physico-chemical properties such as size, hydrophobicity, or charge. This information is very valuable, especially when training data is less abundant. There have been only very few approaches so far that aim at combining these two ideas. Results We propose new string kernels that combine the benefits of physico-chemical descriptors for amino acids with the ones of string kernels. The benefits of the proposed kernels are assessed on two problems: MHC-peptide binding classification using position specific kernels and protein classification based on the substring spectrum of the sequences. Our experiments demonstrate that the incorporation of amino acid properties in string kernels yields improved performances compared to standard string kernels and to previously proposed non-substring kernels. Conclusions In summary, the proposed modifications, in particular the combination with the RBF substring kernel, consistently yield improvements without affecting the computational complexity. The proposed kernels therefore appear to be the kernels of choice for any protein sequence-based inference. Availability Data sets, code and additional information are available from Implementations of the developed kernels are available as part of the Shogun toolbox.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8077
Deposited By:Christian Widmer
Deposited On:17 March 2011