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Christian Widmer, Nora C Toussaint, Yasemin Altun, Oliver Kohlbacher, and Gunnar Rätsch (2010)

Novel Machine Learning Methods for MHC Class I Binding Prediction

In: Pattern Recognition in Bioinformatics 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings, ed. by Tjeerd M.H. Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori and Tom Heskes, vol. 6282, pp. 98-109, Springer. Lecture Notes in Computer Sciences.

MHC class I molecules are key players in the human immune system. They bind small peptides derived from intracellular proteins and present them on the cell surface for surveillance by the immune system. Prediction of such MHC class I binding peptides is a vital step in the design of peptide-based vaccines and therefore one of the major problems in computational immunology. Thousands of different types of MHC class I molecules exist, each displaying a distinct binding specificity. The lack of sufficient training data for the majority of these molecules hinders the application of Machine Learning to this problem. We propose two approaches to improve the predictive power of kernel-based Machine Learning methods for MHC class I binding prediction: First, a modification of the Weighted Degree string kernel that allows for the incorporation of amino acid properties. Second, we propose an enhanced Multitask kernel and an optimization procedure to fine-tune its kernel parameters. The combination of both approaches yields improved performance, which we demonstrate on the IEDB benchmark data set.
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