Senior Staff Fellow — Division of Bioinformatics and Biostatistics

Dong Wang, Ph.D.
(870) 543-7121
[email protected]  

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About  |  Publications 

Background

Dr. Dong Wang received his Ph.D. in genetics in 2003 and his Ph.D. in statistics in 2006 from Iowa State University. He was a faculty member in the Department of Statistics, University of Nebraska-Lincoln, from 2006 to 2014 with the rank of assistant professor and later, associate professor. He worked for three years as the leader of Statistics and Mathematics Group at Dow AgroSciences between 2014 and 2016. He joined NCTR in December 2016 as a senior staff fellow in the Biostatistics Branch of the Division of Bioinformatics and Biostatistics.

Research Interests

Dr. Dong Wang has interest and experience in various aspects of statistics and bioinformatics:  risk assessment, statistical genomics, reproducibility, high-dimensional modeling, Bayesian methods, and post-market surveillance. Currently, he is conducting research on 1) measurement error models regarding biomarkers based on deep-sequencing technology, 2) constructing Bayesian networks for drug-induced liver toxicity by integrating in vitro and in vivo data in addition to expert knowledge, and 3) developing functional analysis models for cardiotoxicity.

Professional Societies/National and International Groups

American Statistical Association (ASA)
Member
2003 – Present

ASA Risk Analysis Section
Scientific Program Officer
2019 – Present

Biopharmaceutical Regulatory-Industry Statistics Workshop
Member, Steering Committee
2017 – 2019

Select Publications

Publication titles are linked to text abstracts on PubMed.

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.