Ekapol Chuangsuwanich

The paper develops a deep learning model to classify tumor cells from normal cells using high-content imaging, achieving an AUROC of 0.78 on a dataset of organoid-derived cells.

The paper introduces SMSNet, a deep learning framework for de novo peptide sequencing that identifies thousands of new HLA antigens and phosphopeptides with high accuracy.

MHCSeqNet is a deep learning model for predicting MHC binding affinity, outperforming existing methods and generalizing to new MHC alleles.