Donghan M. Yang

N-Power AI automates sample size and power analysis in clinical trials using an LLM-based framework, achieving perfect accuracy compared to statistical software.

Cmai predicts antigen-antibody interactions from high-throughput BCR sequencing data, revealing immune responses to tumor antigens and ICI treatment outcomes.

This paper develops pMTnet-omni, an AI system predicting TCR-pMHC binding across species and MHC classes, achieving high accuracy and showing clinical utility in predicting immune-related adverse events.

The paper develops a deep learning tool to analyze pathology images and identify TME features predictive of survival in lung cancer.