Director, Division of Bioinformatics and Biostatistics
Weida Tong, Ph.D.
Dr. Weida Tong received his Ph.D. in polymer chemistry from Fudan University (Shanghai, China) in 1989 and completed his postdoctoral fellowship in computational chemistry at the University of Missouri-St. Louis in 1996. He then joined NCTR to develop a toxicological knowledge base for safety evaluation. In 2002, Dr. Tong became Director of a newly formed Center of Excellence for Bioinformatics (formerly Toxicoinformatics). More recently he was named the Director of the Division of Bioinformatics and Biostatistics. He also holds several adjunct positions at universities, including an associate professorship at Rutgers University in Princeton, New Jersey and a full professorship at the University of Arkansas at Little Rock.
His research division is involved in developing bioinformatics methodologies and standards to support FDA research and regulation for regulatory and translational sciences. Three of the most visible projects from his group are:
- Development of the FDA bioinformatics system, ArrayTrack™ suite, to support FDA review and research on pharmacogenomics.
- Leading the effort on the Microarray Quality Control (also known as MAQC) consortium to develop standards for translational science and personalized medicine.
- Development of a Liver Toxicity Knowledge Base (also known as LTKB) for drug safety.
In addition, his research division specializes in molecular modeling and quantitative structure-activity relationships with specific emphasis on the prediction of endocrine disruptors and human leukocyte antigen (HLA) -related adverse drug reactions. Dr. Tong has published over 230 papers and book chapters, and has routinely been invited to present at national and international conferences.
Professional Societies/National and International Groups
MidSouth Computational Biology and Bioinformatics Society
2002 – Present
Society of Toxicology
2008 – Present
Publication titles are linked to text abstracts on PubMed.
A Model to Predict Severity of Drug-Induced Liver Injury in Humans.
Chen M., Borlak J., and Tong W.
Hepatology. 2016, 64(3):931-40.
DILIrank: The Largest Reference Drug List Ranked by the Risk for Developing Drug-Induced Liver Injury in Humans.
Chen M., Suzuki A., Thakkar S., Yu K., Hu C., and Tong W.
Drug Discov Today. 2016, 21(4):648-53. doi: 10.1016/j.drudis.2016.02.015. Epub 2016 Mar 3.
A Testing Strategy to Predict Risk for Drug-Induced Liver Injury in Humans Using High-Content Screen Assays and the 'Rule-of-Two' Model.
Chen M., Tung C.W., Shi Q., Guo L., Shi L., Fang H., Borlak J., and Tong W.
Arch Toxicol. 2014, 88(7):1439-49.
Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs.
Chen M., Hong H., Fang H., Kelly R., Zhou G., Borlak J., and Tong W.
Toxicol Sci. 2013, 136(1):242-9.
High Lipophilicity and High Daily Dose of Oral Medications are Associated with Significant Risk for Drug-Induced Liver Injury.
Chen M., Borlak J., and Tong W.
Hepatology. 2013, 58(1):388-96.
The Liver Toxicity Knowledge Base: A Systems Approach to a Complex End Point.
Chen M., Zhang J., Wang Y., Liu Z., Kelly R., Zhou G., Fang H., Borlak J., and Tong W.
Clin Pharmacol Ther. 2013, 93(5):409-12.
A Decade of Toxicogenomic Research and its Contribution to Toxicological Science.
Chen M., Zhang M., Borlak J., and Tong W.
Toxicol Sci. 2012, 130(2):217-28.
FDA-Approved Drug Labeling for the Study of Drug-Induced Liver Injury.
Chen M., Vijay V., Shi Q., Liu Z., Fang H., and Tong W.
Drug Discov Today. 2011, 16(15-16):697-703.
Contact information for all lab members:
Minjun Chen, Ph.D.
Weigong Ge, M.S.
Binsheng Gong, Ph.D.
Huixiao Hong, Ph.D.
Baitang Ning, Ph.D.
Senior Computational Scientist
Shraddha Thakkar, MSc, MS, Ph.D.
Dong Wang, Ph.D.
Senior Staff Fellow
Wenming Xiao, Ph.D.
Joshua Xu, Ph.D.
Gokhan Yavas, Ph.D.
Wei (Vivian) Zhuang, Ph.D.
Wen Zou, Ph.D.
- Contact Information
- Weida Tong
- (870) 543-7391