… network modeling showed certain advantages when dealing with big toxicity data, the
resulted neural network models … In vitro assays often investigate mechanistically relevant …

… Finally, we exemplified molecular mechanism of HILI by a case study of Bupleurum L. (… thus
it is difficult to explore the specific mixture activity and toxicity of plant ingredients just through …

… in the case of multitask networks by combining mechanistically … , particularly with respect to
sparse toxicology data, will be … With this approach and our case studies, we have contributed …

… use Bayesian network modeling to develop a qAOP network and how … ) can be found in
several case studies. We describe these … correlation with a mechanistic function may increase the …

… performance metrics as shown in Supplementary Figure S2, we selected the top two
algorithms, DNN and RF, for further optimization and chemical toxicity classification in this study. …

… network model, a task that has proved difficult across many domains. Here we show how the
binding mechanism learned by deep neural network models can … We find that networks that …

… toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk.
In many cases, the mechanism behind the toxicity from … in two case studies employing four …

… KEs in terms of mechanistic knowledge supported by empirical … The main aim of this study
was to develop an AOP network for … The uncertainty of the network model partly arises from the …

… network model of QFPD, established by predicting and collecting the targets of identified
compounds, indicated a pivotal role … the syndrome of dirty-toxicity blocking lung and the …

… study, an integrated statistical network model … case studies against different cancer types
(HCC, LUAD, and BLCA) indicate that systems pharmacology approach applied in this study is …