Chia-Hsuan Chang

Topic: Integrative Construction of Biomedical Knowledge Graph for Explainable Drug Discovery Using Large Language Models

Project Summary: Biological pathways are crucial for understanding diseases and discovering therapeutics, but the traditional reliance on manual curation limits scalability and contextual specificity. With the rapid increase in biomedical literature, there is a pressing need for automated, context-sensitive approaches. As such, our goal is to develop a scalable, context-aware biomedical knowledge graph. This graph will integrate data mined from approximately 38 million PubMed articles, enriched by large language models (LLMs) and curated biomedical databases such as KEGG and Reactome. It will enable precise querying for biological mechanisms and support drug discovery efforts. Additionally, we will introduce a graphaugmented LLM agent designed for mechanism discovery and therapeutic biomarker identification. This agent will perform context-driven subgraph retrieval and prioritize novel, biologically plausible connections. By providing transparent and interpretable reasoning backed by structured biomedical knowledge, our integrated framework will enhance context-aware mechanism discovery and biomarker identification, significantly advancing explainable drug discovery and therapeutic targeting.

Biography: Dr. Chia-Hsuan Chang is a postdoctoral associate in the Clinical NLP lab, led by Dr. Hua Xu, at the Department of Biomedical Informatics and Data Science at Yale School of Medicine. His research focuses on natural language processing (NLP) and large language models (LLMs), particularly their applications in enhancing healthcare and literature-based discovery. He obtained a Ph.D. in Information Systems from National Sun Yat-sen University, under the advice of Dr. San-Yih Hwang. Previously, he was a postdoctoral researcher in the College of Computing & Informatics at Drexel University, mentored by Dr. Christopher Yang.

Yale-Mentor Professor Hua Xu, BI-Mentor Dr. Jon Hill