Supervisory Mathematical Statistician, Biostatistics Branch Chief — Division of Bioinformatics and Biostatistics

Dong Wang, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov  

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About  |  Publications 

Background

Dr. Dong Wang received a Ph.D. in genetics in 2003 and a Ph.D. in statistics in 2006 from Iowa State University. He was a faculty member in the Department of Statistics, University of Nebraska-Lincoln, with the rank of assistant professor and later, associate professor. He worked for three years as the leader of the Statistics and Mathematics Group at Dow AgroSciences. He joined NCTR in 2016 as a senior staff fellow in the Biostatistics Branch of the Division of Bioinformatics and Biostatistics. In 2019, Dr. Wang became the branch chief of the Biostatistics Branch.

Research Interests

Dr. Dong Wang has interest and experience in various aspects of statistics and bioinformatics, including statistical machine learning, statistical genomics, predictive toxicology, Bayesian methods, and real-world data and real-world evidence (RWD/RWE). Currently, he is conducting research on:

• Measurement error models regarding biomarkers based on deep-sequencing technology
• Constructing Bayesian networks for drug-induced liver toxicity by integrating in vitro and in vivo data in addition to expert knowledge
• Investigating racial/ethnic disparities in the delivery of critical care using electronic health record (EHR) data
• Domain adaptation for medical applications

Professional Societies/National and International Groups

American Statistical Association (ASA)
Member
2003 – Present

ASA Risk Analysis Section
Scientific Program Officer
2019 – 2021

Biopharmaceutical Regulatory-Industry Statistics Workshop
Member, Steering Committee
2017 – 2020

Select Publications

Statistical Methods for Exploring Spontaneous Adverse Event Reporting Databases for Drug-Host Factor Interactions.
Lu Z., Suzuki A., and Wang, D.
BMC Medical Research Methodology. 2023, 23(1), p.71. doi: 10.1186/s12874-023-01885-w.

Optimize and Strengthen Machine Learning Models Based on In Vitro Assays with Mechanistic Knowledge and Real-World Data.
Mahanama T.V., Biswas A., and Wang D. 
Machine Learning and Deep Learning in Computational Toxicology. 2023, 183-197. 

A Targeted Simulation-Extrapolation Method for Evaluating Biomarkers Based on New Technologies in Precision Medicine.
Wang D., Wang S.J., Xu J., and Lababidi S. 
Pharmaceutical Statistics. 2023, 21(3):584-598. doi: 10.1002/pst.2187. 

A Verified Genomic Reference Sample for Assessing Performance of Cancer Panels Detecting Small Variants of Low Allele Frequency.
Jones W., Gong B., Novoradovskaya N., Li D., Kusko R., et al.
Genome Biology. 2021, 22(1), pp.1-38.

Medical Information Mart for Intensive Care: A Foundation for the Fusion of Artificial Intelligence and Real-World Data.
Rogers P., Wang D., and Lu Z. 
Frontiers in Artificial Intelligence. 2021, 4:691626, doi: 10.3389/frai.2021.691626.

Integrating Adverse Outcome Pathways (AOPs) and High Throughput In Vitro Assays for Better Risk Evaluations, a Study With Drug-Induced Liver Injury (DILI).
Khadka K.K., Chen M., Liu Z., Tong W., and  Wang D.
ALTEX. 2020, 37(2): 187-196.

In Silico Prediction of the Point of Departure (POD) with High-Throughput Data.
Wang D.
Adv Comput Toxicol. 2019, 229-313.

Infer the In Vivo Point of Departure With ToxCast In Vitro Assay Data Using a Robust Learning Approach.
Wang D.
Arch Toxicol. 2018, 92(9), 2913-2922.

A Strategy for Evaluating Biomarkers Based on Emerging Technologies Using a Measurement Error Framework (PDF download).
Wang D.
PhUSE Connect USA. 2018, Raleigh NC.

Characterization of Founder Viruses in Very Early SIV Rectal Transmission.
Yuan Z., Ma F., Demers A.J., Wang D., Xu J., Lewis M.G., and Li Q.
Virology. 2017, 24:97-105.

Arabidopsis MSH1 Mutation Alters the Epigenome and Produces Heritable Changes in Plant Growth.
Virdi K.S., Laurie J.D., Xu Y.Z., Yu J., Shao M.R., Sanchez R., Kundariya H., Wang D., Riethoven J.M., Wamboldt Y., Arrieta-Montiel M.P., Shedge V., and Mackenzie S.A.
Nat Comm. 2016, 6(6836).

The Effects of Nonnormality on the Analysis of Supersaturated Designs: A Comparison of Stepwise, SCAD and Permutation Test Methods.
Koh W.Y., Eskridge K.M., and Wang D.
J Stat Comput Simulation. 2013, 83:158-166.

Prediction of Genetic Values of Quantitative Traits with Epistatic Effects in Plant Breeding Populations.
Wang D., Salah El-Basyoni I., Stephen Baenziger P., Crossa J., Eskridge K.M., and Dweikat I.
Heredity. 2012, 109:313-319.  

Anticancer Peptidylarginine Deiminase (PAD) Inhibitors Regulate the Autophagy Flux and the Mammalian Target of Rapamycin Complex 1 Activity.
Wang Y., Li P., Wang S., Hu J., Chen X.A., Wu J., Fisher M., Oshaben K., Zhao N., Gu Y., Wang D., Chen G., and Wang Y.
J Biol Chem. 2012, 287: 25941-25953. 

Identifying QTLs and Epistasis in Structured Plant Populations Using Adaptive Mixed LASSO.
Wang D., Eskridge K.M., and Crossa J.
J Agric Biol Environ Stat. 2011, 16: 170-184.  

Development of an Internet Based System for Modeling Biotin Metabolism Using Bayesian Networks.
Zhou J., Wang D., Schlegel V., and Zempleni J.
Comput Methods Programs Biomed. 2011, 104:254-259.

Bayesian Mixture Structural Equation Modeling in Multiple-Trait QTL Mapping.
Mi X., Eskridge K., Wang D., Baenziger P.S., Campbell B.T., Gill K.S., and Dweikat I.
Genet Res. 2010, 92: 239-250.

Structural Equation Modeling of Gene-Environment Interactions in CHD.
Mi X., Eskridge K., Varghese G., and Wang D.
Ann Hum Genet. 2011, 75:255-265.   

Modeling Epigenetic Modifications Under Multiple Treatment Conditions.
Wang D.
Comp Stat Data Anal. 2010, 54: 1179-1189.