Fuhai Li, PhD

  • 2217 Citations
  • 21 h-Index
20052021

Research output per year

If you made any changes in Pure these will be visible here soon.

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 Dive into the research topics where Fuhai Li is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 2 Similar Profiles

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects

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 proceedingChapter

  • 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 journalArticle

  • 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 journalArticle

    Open Access
  • 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 journalArticle

  • 10 Scopus citations

    Systematic Identification of Druggable Epithelial-Stromal Crosstalk Signaling Networks in Ovarian Cancer

    Yeung, T. L., Sheng, J., Leung, C. S., Li, F., Kim, J., Ho, S. Y., Matzuk, M. M., Lu, K. H., Wong, S. T. & Mok, S. C., Mar 1 2019, In : Journal of the National Cancer Institute. 111, 3, p. 272-282 11 p.

    Research output: Contribution to journalArticle

  • 5 Scopus citations