The primary research interest of Qian Zhu, Ph.D., is in translational biomedical and clinical informatics and big data. The development of computer and Web-based technology has resulted in an accumulation of a high volume of biomedical and clinical data. Leveraging informatics approaches and emerging computational and online technologies is the main challenge in biomedical and clinical informatics fields. The goal is to facilitate meaningful use of the big data to better serve drug discovery and patient care.

Dr. Zhu's focus includes data normalization with biomedical terminologies to facilitate data integration and reuse. For example, SNOMED Clinical Terms (SNOMED-CT), RxNorm, Logical Observation Identifiers Names and Codes (LONIC) and Unified Medical Language System (UMLS) are used, as well as standardized data models, such as clinical element models (CEMs).

Another area of interest, high-throughput phenotyping, is used to leverage high-throughput computational technologies for efficient use of health information data to support biomedical and clinical association study.

Additionally, Dr. Zhu is interested in both personalized drug repurposing by utilizing pharmacogenomics data, biomedical data and electronic medical records (EMRs) and personalized medicine by aligning EMRs and genetic testing information. Other areas of interest include biomedical data formal representation through Web ontology language (OWL), leveraging cheminformatics algorithms to deal with biomedical and clinical problems, and Linked Open Drug Data (LODD).

Dr. Zhu is a member of the American Medical Informatics Association. She has served as an editor, associate editor, editorial board member and reviewer for a number of core journals and proceedings on biomedical informatics. She has also been a member and organizer of several scientific program committees for national and international conferences and workshops.

Focus areas

  • Computational drug discovery. This involves large-scale data mining and semantic prediction applied in drug discovery. The goal is to develop large-scale semantic online resources and tools for mining large volumes of heterogeneous drug discovery information related to diseases, side effects and other markers using semantic inference techniques. This includes investigating computational drug repurposing for cancer therapy. The goal is to leverage large-scale pharmacogenomics data, gene expression data, patient data and advanced informatics approaches and technologies to identify novel cancer drug candidates.
  • Computational individualized medicine. This involves utilizing genetic testing information and EMRs to suggest appropriate genetic tests for individualized patients.
  • Pharmacogenomics data standardization. The aim is to create a Pharmacogenomics Research Network (PGRN) resource focused on the codification of standardized phenotype definitions and relationships in coordination with other established government-funded efforts.
  • High-throughput phenotyping. This research area identifies a cohort of patients based on pre-defined algorithms that specify certain diseases, symptoms or clinical findings and converts phenotype algorithms into machine-readable syntax, querying it against EMRs.
  • Linked Drug Open Data. The aim is to discover drug linkages among diverse drug resources on the Web, which supports translational biomedical research.

Significance to patient care

Dr. Zhu's research translates to patient care in three important areas: data standardization, phenotyping and clinical decision system development. The goal is to facilitate clinical data sharing and reuse, enable patient data retrieval to support clinical study, and assist physicians in making clinical decisions.


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