Jie Liu Ph.D.
Staff Fellow — Division of Bioinformatics and Biostatistics
Jie Liu, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov
Back to NCTR Principal Investigator page
Background
Dr. Jie Liu received a master’s degree in immunology from Peking Union Medical College in China and a Ph.D. in bioinformatics from the University of Arkansas at Little Rock. She then conducted postdoctoral research at FDA’s Center for Food Safety and Applied Nutrition and worked as a research scientist at Altamira, LLC. She joined FDA’s National Center for Toxicological Research (NCTR) in 2020 as a bioinformatician in the Division of Bioinformatics and Biostatistics.
Research Interests
Dr. Liu’s specialized research focuses on applying machine learning and deep learning techniques for risk evaluation and safety assessment. Dr. Liu has developed toxicity databases and computational models for liver and other organ toxicity prediction by integrating data from multiple sources. Recently, her works include developing machine learning models for rat multigeneration reproductive toxicity prediction; constructing a random forest model to predict SARS-CoV-2 main protease binding activity for drug repurposing for COVID-19 treatment; and building machine learning and deep learning models for mu opioid receptor (MOR) binding activity prediction for potentially assisting in the development of drugs targeting MOR.
Professional Societies/National and International Groups
Society of Toxicology
Member
2015 – 2016
Selected Publications
Machine Learning and Deep Learning Approaches for Enhanced Prediction of hERG Blockade: A Comprehensive QSAR Modeling Study.
Liu J., Khan M.K.H., Guo W., Dong F., Ge W., Zhang C., Gong P., Patterson T.A., and Hong H.
Expert Opin Drug Metab Toxicol. 2024, 1-20. doi: 10.1080/17425255.2024.2377593.
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.
Developing a SARS-CoV-2 Main Protease Binding Prediction Random Forest Model for Drug Repurposing for COVID-19 Treatment.
Liu J., Xu L., Guo W., Li Z., Khan M.K.H., Ge W., Patterson T.A., and Hong H.
Exp Biol Med (Maywood). 2023, 248(21):1927-1936. doi: 10.1177/15353702231209413.
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.
Exp Biol Med (Maywood). 2023, 248(21):1952-1973. doi: 10.1177/15353702231209421.
Machine Learning Models for Rat Multigeneration Reproductive Toxicity Prediction.
Liu J., Guo W., Dong F., Aungst J., Fitzpatrick S., Patterson T.A., and Hong H.
Front Pharmacol. 2022, 13:1018226. doi: 10.3389/fphar.2022.1018226.
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 (Basel). 2022, 12(19):3376. doi: 10.3390/nano12193376.
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.
BPA Replacement Compounds: Current Status and Perspectives.
Ji Z., Liu J., Sakkiah S., Guo W., and Hong H.
ACS Sustainable Chem. Eng. 2021, 9, 6:2433-2446; doi: 10.1021/acssuschemeng.0c09276.
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.
Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations.
Sakkiah S., Selvaraj C., Guo W., Liu J., Ge W., Patterson T.A., and Hong H.
Int J Mol Sci. 2021, 22(17):9371; doi: 10.3390/ijms22179371.
Utility of Generational Developmental and Reproductive Toxicity and Juvenile Animal Study Protocols for the Infant Safety Assessment of Food Contact Materials.
Neal-Kluever A., Nartey Q., Aungst J., Basso F., Davis-Bruno K., Elayan I., Liu J., Ravindran A., and Wu Y.
Toxicol Res and Appl. 2018, 2:1-21; doi: 10.1177/2397847318806043.
Predicting Organ Toxicity Using In Vitro Bioactivity Data and Chemical Structure.
Liu J., Patlewicz G., Williams A.J., Thomas R.S., and Shah I.
Chem Res Toxicol. 2017, 30(11):2046-2059; doi: 10.1021/acs.chemrestox.7b00084.
Systematically Evaluating Read-Across Prediction and Performance Using a Local Validity Approach Characterized by Chemical Structure and Bioactivity Information.
Shah I., Liu J., Judson R.S., Thomas R.S., and Patlewicz G.
Regul Toxicol Pharmacol. 2016, 79:12-24; doi: 10.1016/j.yrtph.2016.05.008.
Using ToxCast™ Data to Reconstruct Dynamic Cell State Trajectories and Estimate Toxicological Points of Departure.
Shah I., Setzer R.W., Jack J., Houck K.A., Judson R.S., Knudsen T.B., Liu J., Martin M.T., Reif D.M., Richard A.M., Thomas R.S., Crofton K.M., Dix D.J., and Kavlock R.J.
Environ Health Perspect. 2016, 124(7):910-919; doi: 10.1289/ehp.1409029.
Predicting Hepatotoxicity Using ToxCast In Vitro Bioactivity and Chemical Structure.
Liu J., Mansouri K., Judson R.S., Martin M.T., Hong H., Chen M., Xu X., Thomas R.S., and Shah I.
Chem Res Toxicol. 2015, 28(4):738-751; doi: 10.1021/tx500501h.
- Contact Information
- Jie Liu
- (870) 543-7121
- Expertise
-
ExpertiseBioinformaticsBiostatisticsImmunologyToxicology