MACHINE-LEARNING TECHNIQUES FOR PREDICTING SURFACE-PRESENTING PEPTIDES

摘要

The disclosure provides methods for predicting surface-presenting peptides using binding and surface-presentation characteristics. The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression levels and the one or more peptide-presentation metrics are related in accordance with a population-level relationship between expression and presentation. For each peptide of the set of peptides for a tissue sample, a score can be determined using the machine-learning model and genomic and transcriptomic data corresponding to the peptide. The score is predictive of whether a corresponding peptide is a surface-presenting peptide that binds to an MHC molecule and is presented on a cell surface.