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

Wenjing Guo Ph.D.

Bioinformatician — Division of Bioinformatics and Biostatistics

WenjingGuo

Wenjing Guo Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov

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About  |  Publications  |  Lab Members

Background 

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 the Monte Carlo simulation to understand the complicated oil/water/rock system in oil recovery. In 2018, Dr. Guo joined the Division of Bioinformatics and Biostatistics, at FDA's National Center for Toxicological Research (NCTR) as an Oak Ridge Institute for Science and Education (ORISE) fellow. She 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 transferred to an FDA employee. Dr. Guo has 33 publications in scientific journals and has presented at over 10 academic conferences.
 

Research Interests

Dr. Guo’s main 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, and 3) using big data analytics to identify sex disparities in opioid drug safety. 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 safety concerns for drugs that have been used to treat COVID-19 patients.
 

Selected Publications

BERT-Based Language Model for Accurate Drug Adverse Event Extraction from Social Media: Implementation, Evaluation, and Contributions to Pharmacovigilance Practices. 
Dong F., Guo W., Liu J., Patterson T.A., and Hong H. 
Front Public Health. 2024, 12:1392180. DOI: 10.3389/fpubh.2024.1392180. PMID: 38716250.  

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. 
Experimental Biology and Medicine. 2023, 248(21):1952-1973. DOI: 10.1177/1535370223120942. PMID: 38057999.

Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework.
Guo W., Liu J., Dong F., Patterson T.A., and Hong H.
In: Hong, H. (Ed.) Machine Learning and Deep Learning in Computational Toxicology. Computational Methods in Engineering & the Sciences. 2023, pp. 629-654, Springer, Cham. DOI: 10.1007/978-3-031-20730-3_28.

Machine Learning for Predicting Organ Toxicity. 
Liu J., Guo W., Dong F., Patterson T.A., and Hong H.
In: Hong H. (Ed.) Machine Learning and Deep Learning in Computational Toxicology. 2023, pp. 519-537, Cham: Springer International Publishing. DOI: 10.1007/978-3-031-20730-3_22.  

Decision Forest—A Machine Learning Algorithm for QSAR Modeling.
Hong H., Liu J., Guo W., Dong F., Lee M., Xu L., Li Z., Song M., Chen M., Zou W., et al.
In: Hong H. (Ed.) QSAR in Safety Evaluation and Risk Assessment.  2023, pp. 35-48, Academic Press. DOI: 10.1016/B978-0-443-15339-6.00029-1.

EADB—A Database Providing Curated Data for Developing QSAR Models to Facilitate the Assessment of Endocrine Activity.
Dong F., Guo W., Liu J., Xu L., Lee M., Song M., et al. 
In: Hong H. (Ed.) QSAR in Safety Evaluation and Risk Assessment. 2023, pp. 259-272, Academic Press. DOI: 10.1016/B978-0-443-15339-6.00015-1.  

QSAR Models for Predicting In Vivo Reproductive Toxicity.
Liu J., Dong F., Guo W., Li Z., Xu L., Song M., Patterson T. A., and Hong, H.
In: Hong H. (Ed.) QSAR in Safety Evaluation and Risk Assessment. 2023, pp. 315-327, Academic Press. DOI: 10.1016/B978-0-443-15339-6.00013-8.

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. DOI: 10.1177/15353702231214259. PMID: 38102956.

Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials. 
Guo W., Liu J., Dong F., Chen R., Das J., Ge W., Xu X., and Hong H.
Nanomaterials. 2022, 12 (19), 3376. DOI: 0.3390/nano12193376 PMID: 36234502.

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.

 

Lab Members

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

Aasma Aslam, Ph.D.
Postdoctoral Fellow

Contact Information
Wenjing Guo
(870) 543-7121
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