Yuchen Li

The paper introduces chemically informed peptide representations to enable machine learning models to learn from molecular structure rather than canonical amino acid symbols, improving prediction of MHC class I binding.

ProtParts clusters protein sequences using BLAST E-values and graph algorithms to create independent partitions, improving model performance by reducing overfitting.

NetAllergen improves allergenicity prediction by integrating MHC-II presentation propensity, outperforming existing methods when sequence similarity to known allergens is low.