Fuhai Li, PhD
Assistant Professor of Pediatrics
School of Medicine
Fuhai Li, PhD, is focused on applying statistical, machine learning, deep learning and data mining approaches on diverse biomedical dataset integration and interpretation, to solve the challenges in bioinformatics, systems biology and image informatics.
Fuhai Li, PhD, is an Assistant Professor at the Institute for Informatics and Department of Pediatrics at Washington University School of Medicine in St. Louis. His recent research has focused on the integration and interpretation of diverse and heterogeneous pharmacogenomics data for i) understanding driver genetics/functional (mechanism), ii) discovering novel combinatory therapies to overcome intrinsic/acquired drug resistance (with reduced toxicity and synergy effects), and iii) uncovering and perturbing the tumor-stroma interaction to understand the roles of tumor micro-environment/niche in disease development.
Dr. Li obtained his PhD in Applied Math from Peking University. Then he conducted research as a visiting scholar at Harvard Medical School (HMS)/Brigham Women’s Hospital (BWH), where his research focused on developing computational approaches of bio-image informatics to automatically quantify thousands of cellular images to identify potential drugs and targets that can perturb cell cycles and cellular phenotypes. Prior to joining the faculty at Washington University, Dr. Li was an Assistant Professor in the Biomedical Informatics Department at The Ohio State University.
- Integrative large-scale pharmaco genomics analysis for target, signaling network, drug and drug combination discovery
- Genomics data driven tumor-stromal communication discovery and modeling
- Jielin Xu, PhD, Post-Doctoral Fellow, Washington University School of Medicine in St. Louis, Institute for Informatics
- Tianyu Zhang, Visiting Student Scholar, Washington University School of Medicine in St. Louis, Institute for Informatics
- Synergy from Gene Expression and Network Mining (SynGeNet) Method Predicts Synergistic Drug Combinations for Diverse Melanoma Genomic Subtypes. npj Systems Biology and Applications
- Systematic Identification of Druggable Epithelial–Stromal Crosstalk Signaling Networks in Ovarian Cancer. JNCI: Journal of the National Cancer Institute
- Diffusion Mapping of Drug Targets on Disease Signaling Network Elements Reveals Drug Combination Strategies. Pacific Symposium on Biocomputing 2018
- Integrative Network and Transcriptomics-based Approach Predicts Genotype-specific Drug Combinations for Melanoma. AMIA Joint Summits on Translational Science Proceedings 2017 (2017 AMIA TBI Student Paper Award)
- MD-Miner: A Network-based Approach for Personalized Drug Repositioning. Selected Articles from the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: Systems Biology
- DrugComboRanker: Drug Combination Discovery Based on Target Network Analysis. Bioinformatics
- Transcriptome Analysis of Individual Stromal Cell Populations Identifies Stroma-Tumor Crosstalk in Mouse Lung Cancer Model. Cell Reports