MHC-I prediction using a combination of T cell epitopes and MHC-I binding peptides

Tal Vider-Shalit, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

We propose a novel learning method that combines multiple experimental modalities to improve the MHC Class-I binding prediction. Multiple experimental modalities are often accessible in the context of a binding problem. Such modalities can provide different labels of data, such as binary classifications, affinity measurements, or direct estimations of the binding profile. Current machine learning algorithms usually focus on a given label type. We here present a novel Multi-Label Vector Optimization (MLVO) formalism to produce classifiers based on the simultaneous optimization of multiple labels. Within this methodology, all label types are combined into a single constrained quadratic dual optimization problem.We apply the MLVO to MHC class-I epitope prediction. We combine affinity measurements (IC50/EC50), binary classifications of epitopes as T cell activators and existing algorithms. The multi-label vector optimization algorithms produce classifiers significantly better than the ones resulting from any of its components. These matrix based classifiers are better or equivalent to the existing state of the art MHC-I epitope prediction tools in the studied alleles.

Original languageEnglish
Pages (from-to)43-46
Number of pages4
JournalJournal of Immunological Methods
Volume374
Issue number1-2
DOIs
StatePublished - 30 Nov 2011

Bibliographical note

Copyright © 2010 Elsevier B.V. All rights reserved.

Funding

FundersFunder number
National Institute of Allergy and Infectious DiseasesR56AI061062

    Keywords

    • Labels
    • MHC-I binding
    • Regression
    • SVM
    • Supervised learning

    Fingerprint

    Dive into the research topics of 'MHC-I prediction using a combination of T cell epitopes and MHC-I binding peptides'. Together they form a unique fingerprint.

    Cite this