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  8. Wenjing Guo
  1. Science & Research (NCTR)

Wenjing Guo Ph.D.

Visiting Scientist — Division of Bioinformatics and Biostatistics


Wenjing Guo Ph.D.
(870) 543-7121

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About  |  Publications


Dr. Wenjing Guo received a bachelor’s degree in chemical engineering from Dalian University of Technology in China. She then joined the University at Buffalo and received a Ph.D. in chemical engineering focusing on applying a Monte Carlo simulation to understand the complicated oil/water/rock system in oil recovery. In 2018, Dr. Guo joined FDA’s National Center for Toxicological Research (NCTR) in the Division of Bioinformatics and Biostatistics as an Oak Ridge Institute for Science and Education (ORISE) fellow. There she was trained in Dr. Huixiao Hong’s lab on designing and developing machine learning algorithms and software to assist FDA's detection of persistent organic pollutants. In 2021, she was converted to an FDA visiting scientist. Dr. Guo has 17 publications in scientific journals and has presented at over 10 academic conferences.

Research Interests

Dr. Guo’s primary research interest is to apply machine learning and artificial intelligence in various areas including nanomaterials, food and drug safety, and predictive toxicology. Her research includes: 1) designing machine learning algorithms to increase the efficiency of identifying persistent organic pollutant contamination in food and 2) developing deep learning models to enhance the prediction performance of gas adsorption capacities in nanomaterials. Dr. Guo is also interested in using big data analytics to evaluate the safety of drugs. She is working on a project using big data analytics to quantitatively measure the safety concerns for the drugs that have been used to treat COVID-19 patients.

Selected Publications

Informing Selection of Drugs for COVID-19 Treatment Through Adverse Events Analysis.
Guo W., Pan B., Sakkiah S., Ji Z., Yavas G., Lu Y., Komatsu T.E., Lal-Nag M., Tong W., Patterson T.A., and Hong H.
Sci Rep. 2021, 11 (1), 14022. Doi: 10.1038/s41598-021-93500-5. PMID: 34234253.

Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food.
Guo W., Archer J., Moore M., Shojaee S., Zou W., Ge W., Benjamin L., Adeuya A., Fairchild R., and Hong H.
Molecules. 2021, 26(3):685. doi: 10.3390/molecules26030685. PMID: 33525602.

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials.
Ji Z., Guo W., Sakkiah S., Liu J., Patterson T.A., and Hong H.
Nanomaterials (Basel). 2021, 11(6):1599. doi: 10.3390/nano11061599. PMID: 34207026.

Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations.
Sakkiah S., Guo W., Pan B., Ji Z., Yavas G., Azevedo M., Hawes J., Patterson T.A., and Hong H.
Front Chem. 2021, 8:622632. doi: 10.3389/fchem.2020.622632. PMID: 33469527.

Identification of Epidemiological Traits by Analysis of SARS-CoV-2 Sequences.
Pan B., Ji Z., Sakkiah S., Guo W., Liu J., Patterson T.A., and Hong H.
Viruses. 2021, 13(5):764. doi: 10.3390/v13050764. PMID: 33925388.

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model.
Sakkiah S., Leggett C., Pan B., Guo W., Valerio L.G., and Hong H.
J Chem Inf Model. 2020, 60(4):2396-2404. doi: 10.1021/acs.jcim.0c00139. PMID: 32159345.

Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods.
Guo W., Pan B., Sakkiah S., Yavas G., Ge W., Zou W., Tong W., and Hong H.
Int J Environ Res Public Health. 2019, 16(22):4361. doi: 10.3390/ijerph16224361. PMID: 31717330.

QUICK: Quality and Usability Investigation and Control Kit for Mass Spectrometric Data from Detection of Persistent Organic Pollutants.
Guo W., Archer J., Moore M., Bruce J., McLain M., Shojaee S., Zou W., Benjamin L.A., Adeuya A., Fairchild R., and Hong H. 
Int J Environ Res Public Health. 2019, 16(21):4203. doi: 10.3390/ijerph16214203. PMID: 31671576.

Similarities and Differences Between Variants Called with Human Reference Genome HG19 or HG38.
Pan B., Kusko R., Xiao W., Zheng Y., Liu Z., Xiao C., Sakkiah S., Guo W., Gong P., Zhang C., Ge W., Shi L., Tong W., and Hong H.
BMC Bioinformatics. 2019, 20(Suppl 2):101. doi: 10.1186/s12859-019-2620-0. PMID: 30871461.

Computational Prediction Models for Assessing Endocrine Disrupting Potential of Chemicals.
Sakkiah S., Kusko R., Pan B., Guo W., Ge W., Tong W., and Hong H.
J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 2018, 36(4):192-218. doi: 10.1080/10590501.2018.1537132. Epub 2019 Jan 11. PMID: 30633647.

Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations.
Sakkiah S., Kusko R., Pan B., Guo W., Ge W., Tong W., and Hong H.
Front Pharmacol. 2018, 9:492. doi: 10.3389/fphar.2018.00492. PMID: 29867496.


Contact Information
Wenjing Guo
(870) 543-7121
Technology & Discipline
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