Hamid Teimouri

This study shows that feature selection improves peptide binding predictions for TCRs by optimizing model complexity and performance using physicochemical properties and machine learning.

This study improves T cell-antigen specificity prediction by integrating kinetic data into affinity-driven models, enhancing predictions for low-affinity TCR-pMHC interactions.

The paper develops a computational method to predict protein-protein interactions using dipeptide compositions as key features, showing strong correlation across organisms.