Qian Zhu is a staff scientist in the Informatics Core within NCATS’ Division of Preclinical Innovation, where she serves as the team lead of rare disease translational research and oversees multiple rare disease informatics projects to advance rare disease translational research and support the development of the Genetic and Rare Diseases (GARD) Information Center 2.0 and the Rare Disease Alert System (RDAS). Zhu also manages activities conducted by multiple clinical data groups and coordinates their efforts through the Biomedical Data Translator Consortium. She is the chair of NCATS’ Clinical Informatics Interest Group and the co-chair of the Rare Disease Informatics Scientific Interest Group at NIH.
Prior to joining NCATS in 2018, Zhu was a faculty member in the Department of Informatics Systems at the University of Maryland, Baltimore County, and subsequently served as a senior research scientist II in the Division of Public Health at Social & Scientific Systems, Inc., where she directed various biomedical informatics projects by applying artificial intelligence and machine learning approaches.
Zhu earned her doctorate in cheminformatics from the Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences (China), where she worked on automated organic retro-synthesis design. As a research associate at Indiana University, she worked with scientists from Eli Lilly and Company to develop computational programs for drug discovery. Zhu also worked as a research associate under the supervision of Christopher G. Chute, M.D., Dr.P.H., FACMI, at the Mayo Clinic, where she was trained as a professional biomedical informatician able to work with a large volume of clinical data.
Zhu’s primary research focuses on biomedical informatics application development for supporting translational research in rare diseases by exploring various types of biomedical and clinical data. This includes rare disease–related data normalization, harmonization, integration and representation; rare disease–relevant information retrieval and extraction from free text; clinical decision support in rare diseases; and computational drug repositioning for rare diseases.
- Precision Information Extraction for Rare Disease Epidemiology at Scale
- Scientific Evidence Based Rare Disease Research Discovery With Research Funding Data in Knowledge Graph
- An Integrative Knowledge Graph for Rare Diseases, Derived From The Genetic and Rare Diseases Information Center (GARD)
- Multi-Layer Framework of Identifying Placenta Related Research Towards Placenta Curated Research Dataset (PCRD) Development for the PAT Project
- Progress Toward a Universal Biomedical Data Translator