A N M Nafiz Abeer

CARTnet improves B-cell epitope prediction using a contact-aware deep learning framework, outperforming existing methods on internal and external datasets.

UPLiFT improves parameter efficiency in fine-tuning protein language models by optimizing activation space instead of network weights, achieving competitive performance with fewer trainable parameters.

This work evaluates factors affecting generalization of GNN-based TCR-pMHC binding predictors to unseen peptides and proposes architectural improvements.

This paper introduces a computational pipeline for antigen discovery that uses filtering and machine learning to identify conserved neutralizing epitopes in viruses like SARS-CoV-2 and RVFV.

This study compares sequence and structure-based models for predicting antibody developability properties using SEC assays, identifying the most effective in silico approach.