Projects per year
Personal profile
Personal profile
Dr. Fuhai Li received his Ph.D. in applied mathematics at Peking University, and did his pre-doctoral and postdoctoral training in Bioinformatics at Harvard Medical School and Houston Methodist Research Institute. Dr. Li has more than seven years of training and experience in bioinformatics on several research projects funded by NIH, DoD, CPRIT, and other public and private funding sources. He aims to bring better patient care and drug development through his highly collaborative research in bioinformatics and computational biology in the emerging field of big data to knowledge. In particular, he aims to address technical and computational challenges in solving disease problems, including: precision medicine for biomarker identification, drug repositioning, drug combination discovery, and personalized drug response prediction by integrating and analyzing large-scale genomic, imaging, clinical, and environmental data; and tumor-microenvironment interaction modeling to uncover and model the roles of the tumor-niche interactions in tumor development, metastasis, and drug resistance through the integration of genomics and imaging data.
Research interests
Systematic integration of diverse and heterogeneous data resources and subsequent discovery of the embedded knowledge from integrative datasets requires the combination of advanced mathematical approaches and domain knowledge in biomedicine. This emerging field fits extremely well with his experience, expertise, and research interests. Through Dr. Li's highly collaborative research in bioinformatics and computational biology in the emerging field of big data, he aims to bring better patient care to the clinic and enhance drug development In particular, he aims to address technical and computational challenges through use of
-Precision medicine for biomarker identification, drug repositioning, and drug combination discovery
-Personalized drug response prediction by integrating and analyzing large-scale genomic, imaging, clinical, and environmental data
-Tumor-microenvironment interaction modeling to uncover and model the roles of the tumor-niche interactions in tumor development, metastasis, and drug resistance through the integration of genomics and imaging data
Education/Academic qualification
Postdoctoral Fellowship, Harvard Medical School
Radiology, Postdoctoral Fellowship, Houston Methodist Research Institute
Applied Mathematical Sciences, PhD, Peking University
External positions
Assistant Professor of Biomedical Informatics, The Ohio State University
Feb 2016 → …Research Area Keywords
- Systems Medicine & Bioinformatics
- Cancer
Fingerprint
- 2 Similar Profiles
Network
Projects
- 3 Finished
-
Modeling tumor-stroma crosstalk in lung cancer to identify targets for therapy
Wong, S. T., Li, F. & Zhao, H.
7/2/15 → 6/30/20
Project: Federal Funding Agencies
-
A Label-Free and Chemical-Selective Microendoscope to Enhance Prostate Cancer Surgical Outcomes
9/30/12 → 9/29/17
Project: Federal Funding Agencies
-
itNETZ: Integrative and Translational Network-based Cellular Signature Analyzer
National Heart Lung and Blood Institute
9/24/11 → 4/12/13
Project: Federal Funding Agencies
Research Output
-
Synergistic drug combination prediction by integrating multiomics data in deep learning models
Zhang, T., Zhang, L., Payne, P. R. O. & Li, F., 2021, Methods in Molecular Biology. Humana Press, Vol. 2194. p. 223-238 16 p. (Methods in Molecular Biology; vol. 2194).Research output: Chapter in Book/Report/Conference proceeding › Chapter
-
Functional characterization of four ATP-binding cassette transporter A3 gene (ABCA3) variants
Hu, J. Y., Yang, P., Wegner, D. J., Heins, H. B., Luke, C. J., Li, F., White, F. V., Silverman, G. A., Sessions Cole, F. & Wambach, J. A., Jul 1 2020, In : Human Mutation. 41, 7, p. 1298-1307 10 p.Research output: Contribution to journal › Article
1 Scopus citations -
Functional genomics of ABCA3 variants
Wambach, J. A., Yang, P., Wegner, D. J., Heins, H. B., Luke, C., Li, F., White, F. V. & Cole, F. S., Oct 2020, In : American Journal of Respiratory Cell and Molecular Biology. 63, 4, p. 436-443 8 p.Research output: Contribution to journal › Article
-
Integrative network analysis identifies potential targets and drugs for ovarian cancer
Zhang, T., Zhang, L. & Li, F., Sep 21 2020, In : BMC Medical Genomics. 13, 132.Research output: Contribution to journal › Article
Open Access1 Scopus citations -
Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes
Regan-Fendt, K. E., Xu, J., DiVincenzo, M., Duggan, M. C., Shakya, R., Na, R., Carson, W. E., Payne, P. R. O. & Li, F., Dec 1 2019, In : npj Systems Biology and Applications. 5, 1, 6.Research output: Contribution to journal › Article
10 Scopus citations