An automated benchmarking platform for MHC class II binding prediction methods.

ABSTRACT: Motivation:Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research. Results:With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby

SUBMITTER: Andreatta M 

PROVIDER: S-EPMC5925780 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature