Fuhai Li

Fuhai Li, PhD

Adjunct Assistant Professor of Computational Biology in Radiology, Academic Institute
Assistant Affiliate Member, Research Institute
Houston Methodist


scholars@houstonmethodist.org
Biography

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.

Description of Research

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 & Training

Postdoctoral Fellowship, Baylor College of Medicine
PhD, Peking University
Postdoctoral Fellowship, Harvard University
Publications

Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer’s disease
Wang, L, Nykänen, NP, Western, D, Gorijala, P, Timsina, J, Li, F, Wang, Z, Ali, M, Yang, C, Liu, M, Brock, W, Marquié, M, Boada, M, Alvarez, I, Aguilar, M, Pastor, P, Ruiz, A, Puerta, R, Orellana, A, Rutledge, J, Oh, H, Greicius, MD, Le Guen, Y, Perrin, RJ, Wyss-Coray, T, Jefferson, A, Hohman, TJ, Graff-Radford, N, Mori, H, Goate, A, Levin, J, Sung, YJ & Cruchaga, C 2024, , Molecular Neurodegeneration, vol. 19, no. 1, 1. https://doi.org/10.1186/s13024-023-00687-4

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data
Feng, J, Goedegebuure, SP, Zeng, A, Bi, Y, Wang, T, Payne, P, Ding, L, DeNardo, D, Hawkins, W, Fields, RC & Li, F 2024, , PLoS Computational Biology, vol. 20, no. 1, e1011785. https://doi.org/10.1371/journal.pcbi.1011785

Deep learning models to predict primary open-angle glaucoma
Zhou, R, Philip Miller, J, Gordon, M, Kass, M, Lin, M, Peng, Y, Li, F, Feng, J & Liu, L 2024, , Stat, vol. 13, no. 1, e649. https://doi.org/10.1002/sta4.649

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
Dong, Z, Zhang, H, Chen, Y, Payne, PRO & Li, F 2023, , Cancers, vol. 15, no. 17, 4210. https://doi.org/10.3390/cancers15174210

Motor Assessment With the STEGA iPad App to Measure Handwriting in Children
Philip, BA, Li, F, Hawkins-Chernof, E, Chen, L, Swamidass, V & Zwir, I 2023, , American Journal of Occupational Therapy, vol. 77, no. 3, 7703205010. https://doi.org/10.5014/ajot.2023.050098

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
Feng, J, Kong, L, Liu, H, Tao, D, Li, F, Zhang, M & Chen, Y 2023, , Advances in Neural Information Processing Systems, vol. 36.

MicroRNA-575 acts as a novel oncogene via targeting multiple signaling pathways in glioblastoma
Gray, A, Cui, T, Bell, EH, McElroy, J, Sebastian, E, Li, F, Geurts, M, Liu, K, Robe, P, Haque, SJ & Chakravarti, A 2022, , Experimental and Molecular Pathology, vol. 128, 104813, pp. 104813. https://doi.org/10.1016/j.yexmp.2022.104813

Weakly activated core neuroinflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer’s disease
Li, F, Eteleeb, AM, Buchser, W, Sohn, C, Wang, G, Xiong, C, Payne, PR, McDade, E, Karch, CM, Harari, O & Cruchaga, C 2022, , Frontiers in Aging Neuroscience, vol. 14, 935279, pp. 935279. https://doi.org/10.3389/fnagi.2022.935279

Generation of dual-gRNA library for combinatorial CRISPR screening of synthetic lethal gene pairs
Tang, S, Wu, X, Liu, J, Zhang, Q, Wang, X, Shao, S, Gokbag, B, Fan, K, Liu, X, Li, F, Cheng, L & Li, L 2022, , STAR Protocols, vol. 3, no. 3, 101556, pp. 101556. https://doi.org/10.1016/j.xpro.2022.101556

Estrogen hormone is an essential sex factor inhibiting inflammation and immune response in COVID-19
Li, F, Boon, ACM, Michelson, AP, Foraker, RE, Zhan, M & Payne, PRO 2022, , Scientific Reports, vol. 12, no. 1, 9462, pp. 9462. https://doi.org/10.1038/s41598-022-13585-4

How Powerful are K-hop Message Passing Graph Neural Networks
Feng, J, Chen, Y, Li, F, Sarkar, A & Zhang, M 2022, . in S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho & A Oh (eds), Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Advances in Neural Information Processing Systems, vol. 35, Neural information processing systems foundation, 36th Conference on Neural Information Processing Systems, NeurIPS 2022, New Orleans, United States, 11/28/22.

PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Dong, Z, Zhang, M, Li, F & Chen, Y 2022, , Proceedings of Machine Learning Research, vol. 162, pp. 5360-5377.

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model
Feng, J, Zhang, H & Li, F 2021, , BMC bioinformatics, vol. 22, no. 1, 47. https://doi.org/10.1186/s12859-020-03850-6

Predicting mortality risk for preterm infants using random forest
Lee, J, Cai, J, Li, F & Vesoulis, ZA 2021, , Scientific Reports, vol. 11, no. 1, 7308. https://doi.org/10.1038/s41598-021-86748-4

Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
Feng, J, Lee, J, Vesoulis, ZA & Li, F 2021, , npj Digital Medicine, vol. 4, no. 1, 108. https://doi.org/10.1038/s41746-021-00479-4

Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
Li, F, Michelson, AP, Foraker, R, Zhan, M & Payne, PRO 2021, , BMC Medical Informatics and Decision Making, vol. 21, no. 1, 15. https://doi.org/10.1186/s12911-020-01373-x

Synergistic drug combination prediction by integrating multiomics data in deep learning models
Zhang, T, Zhang, L, Payne, PRO & Li, F 2021, . in Methods in Molecular Biology. vol. 2194, Methods in Molecular Biology, vol. 2194, Humana Press, pp. 223-238. https://doi.org/10.1007/978-1-0716-0849-4_12

Predicting Anticancer Drug Response With Deep Learning Constrained by Signaling Pathways
Zhang, H, Chen, Y & Li, F 2021, , Frontiers in Bioinformatics, vol. 1, 639349. https://doi.org/10.3389/fbinf.2021.639349

Functional genomics of ABCA3 variants
Wambach, JA, Yang, P, Wegner, DJ, Heins, HB, Luke, C, Li, F, White, FV & Cole, FS 2020, , American Journal of Respiratory Cell and Molecular Biology, vol. 63, no. 4, pp. 436-443. https://doi.org/10.1165/rcmb.2020-0034MA

Integrative network analysis identifies potential targets and drugs for ovarian cancer
Zhang, T, Zhang, L & Li, F 2020, , BMC Medical Genomics, vol. 13, no. Suppl 9, 132, pp. 132. https://doi.org/10.1186/s12920-020-00773-2