Zhejun Kuang

This paper introduces RLAgent, an LLM-based framework for RNA-ligand interaction modeling that enables interactive, code-free prediction at the RNA region level.

This paper introduces TAPPI, a deep learning framework that assesses trends in protein-protein interaction variations caused by genetic mutations, achieving state-of-the-art performance and biological relevance.

This study benchmarks 15 DNA methylation age prediction methods across 142 datasets, finding that model performance depends on training data characteristics and providing a resource for future research.