Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.

Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding predictions. Here, we train support vector machine classifier (SVMC) models on physicochemical sequence-based and structure-based descriptor sets to predict peptide binding to a well-studied model mouse MHC I allele, H-2D.

U.S. Army disease and nonbattle injury model, refined in Afghanistan and Iraq.

Previous analysis of Operation Desert Shield/Operation Desert Storm data yielded a disease and nonbattle injury (DNBI) model using distinct 95th percentile daily admission rates during the three phases of a war-fighting operation to predict medical requirements. This study refines the model with data from Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF). Inpatient health care records of U.