Huixiao Hong Ph.D.

Branch Chief — Division of Bioinformatics and Biostatistics

Huixiao Hong, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov

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Background

Dr. Huixiao Hong received a Ph.D. from Nanjing University in China where he was also an associate professor and director of the Laboratory of Computational Chemistry. He conducted post-doctoral research at the Maxwell Institute at Leeds University, England. Dr. Hong then became a visiting scientist at the National Cancer Institute at the National Institutes of Health and held a research scientist position at Sumitomo Chemical Company in Japan. He was a senior computational scientist at ROW Science and managed the Bioinformatics Division at Z-Tech, an ICFI company, at FDA’s National Center for Toxicological Research (NCTR) from 2002–2007. From 2007–2018, he was an NCTR research chemist and a supervisory research chemist. He served as the Bioinformatics Branch Chief in the Division of Bioinformatics and Biostatistics from 2018 and has served as a Senior Biomedical Research and Biomedical Product Assessment Service (SBRBPAS) expert since 2021. He has more than 280 publications and has received many awards during his career.

Research Interests

Dr. Hong has a wide range of research interests, including chemoinformatics, computational chemistry, next-generation sequencing data analysis, genome-wide association studies, proteomics, and systems biology. His current research goals are to generate safety signatures of drugs for the treatment of COVID-19 patients via big data analytics and artificial intelligence, and to identify candidate polypharmacy drugs for repurposing for COVID-19 treatment through computational chemistry approaches. Additionally, he is looking to construct an androgenic-activity database to enhance the endocrine disruptor knowledge base and to develop a knowledge base for management of opioid agonist and antagonist activities of chemicals. Dr. Hong is also working to develop predictive models using a variety of machine learning and deep learning algorithms to assist in the safety evaluation of FDA-regulated products.

National and International Groups

8th FDA Scientific Computing Days
Co-chair
2020

AR-BIC 10th Annual Conference
Session Chair
2024

MidSouth Computational Biology and Bioinformatics Society 19th Annual Meeting
Session Chair
2023

OpenTox
Steering Committee, Member
2018 – Present

Workshop on Cheminformatics Resources of U.S. Governmental Organizations
Co-chair
2023

Select Publications

Towards a Light-Mediated Gene Therapy for the Eye using Caged Ethinylestradiol and the Inducible Cre/lox System.
Kiy Z., Chaud J., Xu L., Brandhorst E., Kamali T., Vargas C., Keller S., Hong H., Specht A., and Cambridge S.
Angew Chem Int Ed Engl. 2024, 63(9):e202317675.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.
Li Z., Huang R., Xia M., Patterson T.A., and Hong H.
Biomolecules. 2024, 14(1):72.

RxNorm for Drug Name Normalization: A Case Study of Prescription Opioids in the FDA Adverse Events Reporting System.
Le H., Chen R., Harris S., Fang H., Lyn-Cook B., Hong H., Ge W., Rogers P., Tong W., and Zou W.
Front Bioinform. 2024, 3:1328613.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials.
Tang W., Zhang X., Hong H., Chen J., Zhao Q., and Wu F.
Nanomaterials (Basel). 2024, 14(2):155.

Multi-Omics Data Integration Using Ratio-Based Quantitative Profiling with Quartet Reference Materials.
Zheng Y., Liu Y., Yang J., Dong L., Zhang R., Tian S., Yu Y., Ren L., Hou W., Zhu F., Mai Y., Han J., Zhang L., Jiang H., Lin L., Lou J., Li R., Lin J., Liu H., Kong Z., Wang D., Dai F., Bao D., Cao Z., Chen Q., Chen Q., Chen X., Gao Y., Jiang H., Li B., Li B., Li J., Liu R., Qing T., Shang E., Shang J., Sun S., Wang H., Wang X., Zhang N., Zhang P., Zhang R., Zhu S., Scherer A., Wang J., Wang J., Huo Y., Liu G., Cao C., Shao L., Xu J., Hong H., Xiao W., Liang X., Lu D., Jin L., Tong W., Ding C., Li J., Fang X., and Shi L.
Nat Biotechnol. 2023, doi: 10.1038/s41587-023-01934-1.

Deep Learning Methods for Omics Data Imputation.
Huang L., Song M., Shen H., Hong H., Gong P., Deng H.W., and Zhang C.
Biology (Basel). 2023, 12(10):1313.

Machine Learning and Deep Learning for Brain Tumor MRI Image Segmentation.
Khan M.K.H., Guo W., Liu J., Dong F., Li Z., Patterson T.A., and Hong H.
Exp Biol Med (Maywood). 2023, 248(21):1974-1992.

Review of Machine Learning and Deep Learning Models for Toxicity Prediction.
Guo W., Liu J., Dong F., Song M., Li Z., Khan M.K.H., Patterson T.A., and Hong H.
Exp Biol Med (Maywood). 2023, 248(21):1952-1973.

Quartet RNA Reference Materials Improve the Quality of Transcriptomic Data through Ratio-Based Profiling.
Yu Y., Hou W., Liu Y., Wang H., Dong L., Mai Y., Chen Q., Li Z., Sun S., Yang J., Cao Z., Zhang P., Zi Y., Liu R., Gao J., Zhang N., Li J., Ren L., Jiang H., Shang J., Zhu S., Wang X., Qing T., Bao D., Li B., Li B., Suo C., Pi Y., Wang X., Dai F., Scherer A., Mattila P., Han J., Zhang L., Jiang H., Thierry-Mieg D., Thierry-Mieg J., Xiao W., Hong H., Tong W., Wang J., Li J., Fang X., Jin L., Xu J., Qian F., Zhang R., Shi L., and Zheng Y.
Nat Biotechnol. 2023, doi: 10.1038/s41587-023-01867-9.

Distinct Conformations of SARS-CoV-2 Omicron Spike Protein and Its Interaction with ACE2 and Antibody.
Lee M., Major M., and Hong H.
Int J Mol Sci. 2023, 24(4):3774.

Analyzing 3D Structures of the SARS-CoV-2 Main Protease Reveals Structural Features of Ligand Binding for COVID-19 Drug Discovery.
Xu L., Chen R., Liu J., Patterson T.A., and Hong H.
Drug Discov Today. 2023, 28(10):103727.

Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor.
Li Z., Liu J., Dong F., Chang N., Huang R., Xia M., Patterson T.A., and Hong H.
Int J Mol Sci. 2023, 24(8):7042.

The Quartet Data Portal: Integration of Community-Wide Resources for Multiomics Quality Control.
Yang J., Liu Y., Shang J., Chen Q., Chen Q., Ren L., Zhang N., Yu Y., Li Z., Song Y., Yang S., Scherer A., Tong W., Hong H., Xiao W., Shi L., and Zheng Y.
Genome Biol. 2023, 24(1):245.

Correcting Batch Effects in Large-Scale Multiomics Studies Using a Reference-Material-Based Ratio Method.
Yu Y., Zhang N., Mai Y., Ren L., Chen Q., Cao Z., Chen Q., Liu Y., Hou W., Yang J., Hong H., Xu J., Tong W., Dong L., Shi L., Fang X., and Zheng Y.
Genome Biol. 2023, 24(1):201.

Assessments of Tumor Mutational Burden Estimation by Targeted Panel Sequencing: A Comprehensive Simulation Analysis.
Li D., Wang D., Johann D.J. Jr., Hong H., and Xu J.
Exp Biol Med (Maywood). 2023, 248(21):1918-1926.

A Systematic Analysis and Data Mining of Opioid-Related Adverse Events Submitted to the FAERS Database.
Le H., Hong H., Ge W., Francis H., Lyn-Cook B., Hwang Y.T., Rogers P., Tong W., and Zou W.
Exp Biol Med (Maywood). 2023, 248(21):1944-1951.

Quartet DNA Reference Materials and Datasets for Comprehensively Evaluating Germline Variant Calling Performance.
Ren L., Duan X., Dong L., Zhang R., Yang J., Gao Y., Peng R., Hou W., Liu Y., Li J., Yu Y., Zhang N., Shang J., Liang F., Wang D., Chen H., Sun L., Hao L.; Quartet Project Team; Scherer A., Nordlund J., Xiao W., Xu J., Tong W., Hu X., Jia P., Ye K., Li J., Jin L., Hong H., Wang J., Fan S., Fang X., Zheng Y., and Shi L.
Genome Biol. 2023, 24(1):270.

Developing a SARS-CoV-2 Main Protease Binding Prediction Random Forest Model for Drug Repurposing for COVID-19 Treatment.
Liu J., Xu L., Guo W., Li Z., Khan M.K.H., Ge W., Patterson T.A., and Hong H.
Exp Biol Med (Maywood). 2023, 248(21):1927-1936.

Lab Members

Contact information for all lab members:
(870) 543-7121
NCTRResearch@fda.hhs.gov

Minjun Chen, Ph.D.
Staff Fellow

Fan Dong, Ph.D.
Postdoctoral Fellow

Wenjing Guo, Ph.D.
Visiting Scientist

Jie Liu, Ph.D.
Staff Fellow

Wen Zou, Ph.D.
Visiting Scientist

Contact Information: Huixiao Hong; (870) 543-7121

Expertise: Expertise

Approach

Domain

Technology & Discipline

Toxicology