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Jie Liu

Dr. Jie Liu received 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 staff fellow in the  Division of Bioinformatics and Biostatistics. Dr. Liu’s specialized research focuses on  the development of machine learning models and databases for safety evaluation and  risk assessment. She has developed toxicity databases and computational models for  liver and other organ toxicity prediction by integrating data from multiple sources. Dr. Liu’s work also includes the development of machine learning models for in vivo  toxicity prediction, opioid receptor binding activity prediction, and the construction of a  cheminformatics system to manage the extractable and leachable chemicals for medical  devices and their associated toxicity data.

OpenTox Virtual Conference 2023

Endocrine Activity Predictive Models 

Endocrine activity is crucial for human health since endocrine pathways regulate  growth, development, reproduction, metabolism, and tissue function. Endocrine  disrupting chemicals are a group of chemicals may alter the endocrine system and  disrupt endocrine function by mimicking endogenous hormones. Humans and wildlife  are exposed to endocrine disrupting chemicals might alter endocrine functions through  various mechanisms and lead to various adverse effects. Hence, it is important to  identify endocrine disrupting chemicals for improving the public health and protecting  the ecosystem. However, the experiments to identify potential endocrine disrupting  chemicals is time consuming and expensive. Therefore, machine learning is an efficient  and promising approach to screen and predict the potential endocrine disrupting  chemicals. 

In this session, our speakers will talk about the endocrine activity predictive models  developed using various algorithms.