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Xinghua Lu, MD, PhD
- Professor of Biomedical Informatics
Department of Biomedical Informatics (DBMI) | University of Pittsburgh School of Medicine
Biography
Dr. Xinghua Lu is Professor of Biomedical Informatics at the University of Pittsburgh. He was trained in medicine, clinical research, pharmacology, and biomedical informatics, and his career has spanned clinical medicine, bioinformatics, and translational data science. His research focuses on causal discovery, machine learning, precision oncology, systems biology, and translational bioinformatics. He develops computational methods and clinical decision support systems that integrate genomic, molecular, and clinical data to reveal disease mechanisms and support personalized medicine.
Computational & Clinical Expertise
Dr. Lu trained in medicine (specialized in cardiology) and pharmacology (PhD) before moving into biomedical informatics research. His clinical and informatics expertise lies in translational bioinformatics, precision oncology, and the application of machine learning and causal modeling to clinically relevant genomic and molecular data. He develops clinical decision support systems guiding the personalized application of anticancer medicines.
Research Interests
Dr. Lu’s research develops computational and statistical approaches for extracting biological and clinical knowledge from large-scale data. His work spans causal discovery, precision oncology, systems biology, machine learning, and deep learning, with a particular focus on linking genomic alterations to signaling pathways, tumor behavior, and therapeutic response. He also studies translational bioinformatics methods that integrate prior biological knowledge with high-dimensional molecular data. Across these areas, his goal is to move from biomedical big data to mechanistic insight and clinically actionable precision medicine.
Specialties
- Translational Bioinformatics
- Precision Oncology
- Causal Discovery
- Machine Learning in Biomedicine
- Clinical Decision Support Systems
- Cancer Informatics
Education & Training
- M.B./M.D.Shandong Medical University, China1979-1984
- M.S., Clinical Research (Cardiology)Shandong Medical University, China1985-1988
- ResidencyInternal Medicine — Shengli Central Hospital, Dongying, China1984-1985
- Residency, Chief Residency, Emergency MedicineShandong Provincial Hospital, Jinan, China1988-1991
- Ph.S, PharmacologyUniversity of Connecticut Health Center, CT1993-1998
- Postdoctoral Training, Postdoctoral Fellow, Signal Transduction LaboratoryNIEHS1998
- Postdoctoral Training, Research Associate, Department of PharmacologyUniversity of Pittsburgh, Pittsburgh, PA2001-2003
- Postdoctoral Training, ational Library of Medicine FellowUniversity of Pittsburgh, Pittsburgh, PA2001-2003
- Certificate, Biomedical InformaticsUniversity of Pittsburgh, Pittsburgh, PA
Awards & Distinctions
- 1st Place Award, Partners HealthCare Biobank Disease ChallengePartners HealthCare Biobank Disease Challenge2019
- 1st Place Award, SBV IMPROVER Trans-species Network ChallengeSBV IMPROVER2013
- Outstanding Paper Award, AMIA Summit on Translational BioinformaticsAmerican Medical Informatics Association2009
- National Library of Medicine Training FellowshipNational Library of Medicine2001–2003
Representative Publications
Chen L, Wang Y, Cai C, et al., & Lu X. (2024). Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer. Journal of Clinical Oncology, 42(13), 1520–1530.
Ren S, Cooper GF, Chen L, et al., & Lu X. (2024). An interpretable deep learning framework for genome-informed precision oncology. Nature Machine Intelligence, 6, 864–875.
Chen X, Chen L, Kürten CHL, et al., & Lu X. (2022). An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors. PLoS Computational Biology, 18(12), e1010761.
Cai C, Cooper GF, Lu KN, et al., & Lu X. (2019). Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference. PLoS Computational Biology, 15(7), e1007088.
Ding MQ, Chen L, Cooper GF, Young JD, & Lu X. (2018). Precision oncology beyond targeted therapy: Combining omics data with machine learning matches the majority of cancer cells to effective therapeutics. Molecular Cancer Research, 16(2), 269–278.
Research Grants
| Grant / Funding Agency | Role | Grant Number | Years |
|---|---|---|---|
| Interpretable deep learning models for translational medicine (renewal) NIH/NLM | PI | 5R01LM012011 | Ongoing |
Trainees
| Trainee | Degree Program / Role & Years |
|---|---|
| Alexander VanHelene | PhD, Research Advisor (2nd year) |
| Ethan Wolfe | PhD, Research Advisor (1st year) |
| Aodong Qiu | MD/PhD, Research Advisor (3rd year) |