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• W2204599701 abstract "Objective Adverse drug reaction (ADR) can have dire consequences. However, our current understanding of the causes of drug-induced toxicity is still limited. Hence it is of paramount importance to determine molecular factors of adverse drug responses so that safer therapies can be designed. Methods We propose a causality analysis model based on structure learning (CASTLE) for identifying factors that contribute significantly to ADRs from an integration of chemical and biological properties of drugs. This study aims to address two major limitations of the existing ADR prediction studies. First, ADR prediction is mostly performed by assessing the correlations between the input features and ADRs, and the identified associations may not indicate causal relations. Second, most predictive models lack biological interpretability. Results CASTLE was evaluated in terms of prediction accuracy on 12 organ-specific ADRs using 830 approved drugs. The prediction was carried out by first extracting causal features with structure learning and then applying them to a support vector machine (SVM) for classification. Through rigorous experimental analyses, we observed significant increases in both macro and micro F1 scores compared with the traditional SVM classifier, from 0.88 to 0.89 and 0.74 to 0.81, respectively. Most importantly, identified links between the biological factors and organ-specific drug toxicities were partially supported by evidence in Online Mendelian Inheritance in Man. Conclusions The proposed CASTLE model not only performed better in prediction than the baseline SVM but also produced more interpretable results (ie, biological factors responsible for ADRs), which is critical to discovering molecular activators of ADRs." @default.
• W2204599701 created "2016-06-24" @default.
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• W2204599701 date "2014-03-01" @default.
• W2204599701 modified "2023-10-16" @default.
• W2204599701 title "Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning" @default.
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• W2204599701 doi "https://doi.org/10.1136/amiajnl-2013-002051" @default.
• W2204599701 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3932464" @default.
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