首页|Nanjing Medical University Reports Findings in Machine Learning (Hepatic toxicit y prediction of bisphenol analogs by machine learning strategy)
Nanjing Medical University Reports Findings in Machine Learning (Hepatic toxicit y prediction of bisphenol analogs by machine learning strategy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevale nt type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health." The news correspondents obtained a quote from the research from Nanjing Medical University, "However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs' hepatotoxicity using network a nalysis coupled with machine learning models. Considering the structural and fun ctional similarities shared by BPs with Bisphenol A (BPA), we first integrated h epatic disease related genes from multiple databases into BPA-Gene-Phenotype-hep atic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatot oxicity of 20 emerging BPs and provided new insights into how BPs' structure fea tures contributed to biologic functions with limited experimental data. Addition ally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating e vent."
NanjingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesGastroenterologyHealth and MedicineMachin e Learning
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