EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning.

ABSTRACT:

Motivation

Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some phenotype and accordingly develop T-cell mediated immunotherapy treatments. Although the importance of the CDR3 region in TCRs for epitope recognition is well recognized, methods for profiling their interactions in association to a certain disease or phenotype remains less studied. We developed EpicPred to identify phenotype-specific TCR-epitope interactions. EpicPred first predicts and removes unlikely TCR-epitope interactions to reduce false positives using the Open-set Recognition (OSR). Subsequently, multiple instance learning was used to identify TCR-epitope interactions specific to a cancer type or severity level

SUBMITTER: Jeon J 

PROVIDER: S-EPMC11879650 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature