Abstract: ABSTRACTFrameworks to predict in vivo effects by integration of in vitro, in silico and in chemico information using mechanistic insight are needed to meet the challenges of 21st century toxicology. Expert‐based approaches that qualitatively integrate multifaceted data are practiced under the term ’weight of evidence’, whereas quantitative approaches remain rare. To address this gap we previously developed a methodology to design an Integrated Testing Strategy (ITS) in the form of a Bayesian Network (BN). This study follows up on our proo...
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Topics: 
Data mining
Machine learning
Toxicology