Predicting Peptide-MHC Binding Affinities with Imputed Training Data
By A. Rubinsteyn, Tim O’Donnell, N. Damaraju, Jeff Hammerbacher. 2016
10 citations recorded by the source corpus.
Predicting the binding affinity between MHC proteins and their peptide ligands is a key problem in computational immunology. State of the art performance is currently achieved by the allele-specific predictor NetMHC and the pan-allele predictor NetMHCpan, both of which are ensembles of shallow neural networks. We explore an intermediate between allele-specific and pan-allele prediction: training allele-specific predictors with synthetic samples generated by imputation of the peptide-MHC affinity matrix. We find that the imputation strategy is useful on alleles with very little training data. We have implemented our predictor as an open-source software package called MHCflurry and show that MHCflurry achieves competitive performance to NetMHC and NetMHCpan.
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- Travers Ching
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- Daniel S. Himmelstein
- Amr M. Alexandari
- G. Way
- Casey S. Greene
- Brian T Do
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “There is some evidence that this can improve performance [306], but in other cases it was only beneficial when the real training datasets were extremely small [370].”
- Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins (2017, methodology): “These methods have been previously reported to augment their training sets with additional proprietary data, beyond what is in the benchmark training set used in this work [16, 30], so their self-reported performance estimates may be overly optimistic.”