Skip to Main content Skip to Navigation
Other publications

Epitope prediction improved by multitask support vector machines

Laurent Jacob 1, * Jean-Philippe Vert 1, * 
* Corresponding author
Abstract : Motivation: In silico methods for the prediction of antigenic peptides binding to MHC class I molecules play an increasingly important role in the identification of T-cell epitopes. Statistical and machine learning methods, in particular, are widely used to score candidate epitopes based on their similarity with known epitopes and non epitopes. The genes coding for the MHC molecules, however, are highly polymorphic, and statistical methods have difficulties to build models for alleles with few known epitopes. In this case, recent works have demonstrated the utility of leveraging information across alleles to improve the performance of the prediction. Results: We design a support vector machine algorithm that is able to learn epitope models for all alleles simultaneously, by sharing information across similar alleles. The sharing of information across alleles is controlled by a user-defined measure of similarity between alleles. We show that this similarity can be defined in terms of supertypes, or more directly by comparing key residues known to play a role in the peptide-MHC binding. We illustrate the potential of this approach on various benchmark experiments where it outperforms other state-of-the-art methods.
Complete list of metadata

Cited literature [43 references]  Display  Hide  Download
Contributor : Laurent Jacob Connect in order to contact the contributor
Submitted on : Tuesday, February 6, 2007 - 11:52:31 AM
Last modification on : Wednesday, November 17, 2021 - 12:30:52 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 8:34:33 PM


Files produced by the author(s)



Laurent Jacob, Jean-Philippe Vert. Epitope prediction improved by multitask support vector machines. 2007. ⟨hal-00129062⟩



Record views


Files downloads