Automated benchmarking of peptide-MHC class I binding predictions.

ABSTRACT:

Motivation

Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implement

SUBMITTER: Trolle T 

PROVIDER: S-EPMC4481849 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature