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Fang Li, PhD, joined the UTHealth School of Biomedical Informatics (SBMI) as an assistant professor and a faculty member of the Center for Biomedical Semantics and Data Intelligence (BSDI) on October 1, 2021. Before that, Dr. Li received three years of postdoctoral training and worked as a research scientist for one year in SBMI. With a unique interdisciplinary background in both information science and medicine, Dr. Li has a passion to utilize cutting-edge informatics techniques to innovatively solve real-world medical problems based on a profound understanding toward them.

Dr. Li’s main research interests include: (1) biomedical ontology/knowledge graph construction and application, by realizing comprehensive and multi-source semantic knowledge representation, fusion, connection, and reasoning to support novel knowledge discovery (e.g., pathway, signal, and drug target); (2) big data analytics, leveraging advanced statistics and artificial intelligence (AI) techniques to translate large-scale real-world data (electronic health records, health claims, etc.) into actionable solutions for critical clinical issues; (3) machine learning (ML) and deep learning (DL), for the fundamental methodology pillars of current biomedical informatics research, keeping up with ever-evolving algorithms and developing models with optimum efficacy, interpretability and generalizability is one of her most enjoyable priorities. Dr. Li has published more than twenty peer-reviewed journal and conference papers, which involve JAMIA, Alimentary Pharmacology & Therapeutics, JMIR, etc., as the first author and the coauthor, and has served as a guest editor and ad-hoc reviewer for several journals.

“Data-driven (big data) and knowledge-driven (ontology and knowledge graph) explainable AI techniques, coupled with strenuous efforts from both data scientists and clinicians/biomedical researchers, will accelerate the realization of person-centered precision health care in the future,” Dr. Li said.


  • PhD, Information Science, University of Chinese Academy of Sciences, 2017
  • MS, Peking Union Medical College, Pharmacognosy, 2009
  • BS, Huazhong University of Science and Technology, Biomedical Informatics, 2005

Areas of Expertise

  • Biomedical ontologies and knowledge graphs
  • Big data analytics
  • Machine learning and deep learning

Staff Support

Yukiko Bryso | 713-486-3992

Selected Recent Publications

  • Karn Wijarnpreecha, Fang Li* (co-first author), Yang Xiang, Xun Xu, Cong Zhu, Vahed Maroufy, Qing Wang, Wei Tao, Yifang Dang, Huy Anh Pham, Yujia Zhou, Jianfu Li, Xinyuan Zhang, Hua Xu, Burcin Taner, Liu Yang, Cui Tao. Nonselective beta-blockers are associated with a lower risk of hepatocellular carcinoma among cirrhotic patients in the United States. Alimentary Pharmacology & Therapeutics. 2021 Aug;54(4):481-492. doi: 10.1111/apt.16490. PMID: 34224163.
  • Fang Li*, Jingcheng Du*, Yongqun He, Hsing-Yi Song, Mohcine Madkour, Guozheng Rao, Yang Xiang, Yi Luo, Henry W Chen, Sijia Liu, Liwei Wang, Hongfang Liu, Hua Xu, Cui Tao. Time Event Ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events[J]. Journal of the American Medical Informatics Association, 2020 Jul 1;27(7):1046-1056. doi: 10.1093/jamia/ocaa058. PMID: 32626903; PMCID: PMC7647306.
  • Fang Li, Guozheng Rao, Jingcheng Du, Yang Xiang, Yaoyun Zhang, Salih Selek, Jane Elizabeth Hamilton, Hua Xu, Cui Tao. Ontological representation-oriented term normalization and standardization of the Research Domain Criteria[J]. Health Informatics Journal, 2020 Jun; 26(2):726-737. doi: 10.1177/1460458219832059. PMID: 30843449; PMCID: PMC7863676.
  • Yang Xiang, Jingcheng Du, Kayo Fujimoto, Fang Li, John Schneider, Cui Tao. Review of Application of Artificial Intelligence and Machine Learning for HIV Prevention Interventions to Eliminate HIV[J]. The Lancet HIV (accepted), 2021.
  • Yang Xiang, Kayo Fujimoto, Fang Li, Qing Wang, Natascha Del Vecchio, John Schneider, Degui Zhi, Cui Tao. Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection[J]. AIDS, 2021, May 1;35(Suppl 1): S65-S73. doi: 10.1097/QAD.0000000000002784. PMID: 33306549; PMCID: PMC8058230.