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
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Projects
- 3 Finished
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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
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A Label-Free and Chemical-Selective Microendoscope to Enhance Prostate Cancer Surgical Outcomes
9/30/12 → 9/29/17
Project: Federal Funding Agencies
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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
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Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
Li, F., Michelson, A. P., Foraker, R., Zhan, M. & Payne, P. R. O., Jan 7 2021, In: BMC Medical Informatics and Decision Making. 21, 1, 15.Research output: Contribution to journal › Article › peer-review
Open Access4 Scopus citations -
Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model
Feng, J., Zhang, H. & Li, F., Dec 2021, In: BMC bioinformatics. 22, 1, 47.Research output: Contribution to journal › Article › peer-review
Open Access3 Scopus citations -
Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
Feng, J., Lee, J., Vesoulis, Z. A. & Li, F., Dec 2021, In: npj Digital Medicine. 4, 1, 108.Research output: Contribution to journal › Article › peer-review
Open Access -
Predicting mortality risk for preterm infants using random forest
Lee, J., Cai, J., Li, F. & Vesoulis, Z. A., Dec 2021, In: Scientific Reports. 11, 1, 7308.Research output: Contribution to journal › Article › peer-review
Open Access1 Scopus citations -
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
5 Scopus citations