Improving peptide-MHC class I binding prediction for unbalanced datasets.

Published online

Journal Article

BACKGROUND: Establishment of peptide binding to Major Histocompatibility Complex class I (MHCI) is a crucial step in the development of subunit vaccines and prediction of such binding could greatly reduce costs and accelerate the experimental process of identifying immunogenic peptides. Many methods have been applied to the prediction of peptide-MHCI binding, with some achieving outstanding performance. Because of the experimental methods used to measure binding or affinity between peptides and MHCI molecules, however, available datasets are enriched for nonbinders, and thus highly unbalanced. Although there is no consensus on the ideal class distribution for training sets, extremely unbalanced datasets can be detrimental to the performance of prediction algorithms. RESULTS: We have developed a decision-theoretic framework to construct cost-sensitive trees to predict peptide-MHCI binding and have used them to 1) Assess the impact of the training data's class distribution on classifier accuracy, and 2) Compare resampling and cost-sensitive methods as approaches to compensate for training data imbalance. Our results confirm that highly unbalanced training sets can reduce the accuracy of classifier predictions and show that, in the peptide-MHCI binding context, resampling methods do not improve the classifier performance. In contrast, cost-sensitive methods significantly improve accuracy of decision trees. Finally, we propose the use of a training scheme that, when the training set is enriched for nonbinders, consistently improves the overall classifier accuracy compared to cost-insensitive classifiers and, in particular, increases the sensitivity of the classifiers. This method minimizes the expected classification cost for large datasets. CONCLUSION: Our method consistently improves the performance of decision trees in predicting peptide-MHC class I binding by using cost-balancing techniques to compensate for the imbalance in the training dataset.

Full Text

Duke Authors

Cited Authors

  • Sales, AP; Tomaras, GD; Kepler, TB

Published Date

  • September 19, 2008

Published In

Volume / Issue

  • 9 /

Start / End Page

  • 385 -

PubMed ID

  • 18803836

Pubmed Central ID

  • 18803836

Electronic International Standard Serial Number (EISSN)

  • 1471-2105

Digital Object Identifier (DOI)

  • 10.1186/1471-2105-9-385

Language

  • eng

Conference Location

  • England