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  Novel Machine Learning Methods for MHC Class I Binding Prediction

Widmer, C., Toussaint, N., Altun, Y., Kohlbacher, O., & Rätsch, G. (2010). Novel Machine Learning Methods for MHC Class I Binding Prediction. In T. Dijkstra, E. Tsivtsivadze, E. Marchiori, & T. Heskes (Eds.), PRIB 2010: Pattern Recognition in Bioinformatics (pp. 98-109). Berlin, Germany: Springer.

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 Creators:
Widmer, C1, Author              
Toussaint, NC, Author
Altun, Y2, 3, Author              
Kohlbacher, O, Author
Rätsch, G1, Author              
Affiliations:
1Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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 the kernel parameters. The combination of both approaches yields improved performance, which we demonstrate on the IEDB benchmark data set.

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 Dates: 2010-09
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-16001-1_9
 Degree: -

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Title: 5th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2010)
Place of Event: Nijmegen, The Netherlands
Start-/End Date: 2010-09-22 - 2010-09-24

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Title: PRIB 2010: Pattern Recognition in Bioinformatics
Source Genre: Proceedings
 Creator(s):
Dijkstra, THM, Editor
Tsivtsivadze, E, Editor
Marchiori, E, Editor
Heskes, T, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 98 - 109 Identifier: ISBN: 978-3-642-16000-4

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 6282 Sequence Number: - Start / End Page: - Identifier: -