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Session A: (July 7 and July 8)
Session B: (July 9 and July 10)
Short Abstract: Conventional toxicity assessment is usually conducted using indicators such as pathology and clinical chemistry data, which could only detect around 60% of drug-induced liver injury (DILI) cases in the preclinical studies. The agreement between studies on animal models and human clinical trials is often poor. In this paper, we designed a computational framework to predict DILI of drug compound from cell-based CMap gene expression responses of two different cancer cell lines (MCF7 and PC3). The computational framework takes advantage of ensemble feature selection methods to first identify candidate discriminative genomic indicators, and then evaluates the performance of those genomic indicators by ensemble classifiers. Finally, the inherent connections among genomic indicators identified are explored using network analysis. The network analysis is able to discover the redundancy among genomic indicators and benefits the identification of a set of optimum non-redundant genomic indicators, so that improves the DILI prediction. We use the ROC (receiver operating characteristic) curve and AUC (area under the curve) to comprehensively evaluate our methods. The cross-validation results show that our method can achieve high AUCs, indicating the effectiveness of our framework.