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- W2894461861 startingPage "1138" @default.
- W2894461861 abstract "The paradigm of chemical safety assessment is shifting from ‘chemical management focusing on single chemicals’ to ‘product management extending to mixtures and articles’. However, because of the enormous combinatorial complexity, testing the toxicity of all conceivable mixture products is currently not feasible. There exist only few models that allow predicting the synergistic toxicity potentially caused by toxicological interactions among components. In this study, we present a novel approach to qualitatively predict the synergistic toxicity of binary mixtures to Vibrio fischeri. On the basis of information derived from protein–chemical and protein–protein interaction networks, we trained machine learning models for classifying chemical mixtures to have synergistic or nonsynergistic toxicity with accuracies and an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.73. The numbers of shared targets and their neighborhood were found to be the most important features for classifying chemicals into synergistic and nonsynergistic groups." @default.
- W2894461861 created "2018-10-05" @default.
- W2894461861 creator A5007473459 @default.
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- W2894461861 date "2018-09-27" @default.
- W2894461861 modified "2023-10-14" @default.
- W2894461861 title "Prediction of Synergistic Toxicity of Binary Mixtures to Vibrio fischeri Based on Biomolecular Interaction Networks" @default.
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- W2894461861 doi "https://doi.org/10.1021/acs.chemrestox.8b00164" @default.
- W2894461861 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30259734" @default.
- W2894461861 hasPublicationYear "2018" @default.
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