BACKGROUND Drug-induced liver injury (DILI) is a serious concern during drug development and the treatment of human disease. The ability to accurately predict DILI risk could yield significant improvements in drug attrition rates during drug development, in drug withdrawal rates, and in treatment outcomes. In this paper, we outline our approach to predicting DILI risk using gene-expression data from Build 02 of the Connectivity Map (CMap) as part of the 2018 Critical Assessment of Massive Data Analysis CMap Drug Safety Challenge. RESULTS First, we used seven classification algorithms independently to predict DILI based on gene-expression values for two cell lines. Similar to what other challenge participants observed, none of these algorithms predicted liver injury on a consistent basis with high accuracy. In an attempt to improve accuracy, we aggregated predictions for six of the algorithms (excluding one that had performed exceptionally poorly) using a soft-voting method. This approach also failed to generalize well to the test set. We investigated alternative approaches-including a multi-sample normalization method, dimensionality-reduction techniques, a class-weighting scheme, and expanding the number of hyperparameter combinations used as inputs to the soft-voting method. We met limited success with each of these solutions. CONCLUSIONS We conclude that alternative methods and/or datasets will be necessary to effectively predict DILI in patients based on RNA expression levels in cell lines. REVIEWERS This article was reviewed by Paweł P Labaj and Aleksandra Gruca (both nominated by David P Kreil).

中文翻译：

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使用基因表达谱预测药物诱发的肝损伤的多种方法。

背景技术在药物开发和人类疾病的治疗期间，药物引起的肝损伤（DILI）是一个严重的问题。准确预测DILI风险的能力可能会在药物开发过程中的药物消耗率，戒断率和治疗结果方面产生显着改善。在本文中，我们概述了使用连通性图（CMap）的Build 02中的基因表达数据预测DILI风险的方法，这是2018年大规模数据分析CMap药物安全挑战关键评估的一部分。结果首先，我们基于两种细胞系的基因表达值分别使用了七个分类算法来预测DILI。与其他挑战参与者所观察到的相似，这些算法均无法高度一致地预测肝损伤。为了提高准确性，我们使用软投票方法汇总了六种算法的预测结果（不包括执行异常的算法）。这种方法也不能很好地推广到测试集。我们研究了替代方法，包括多样本归一化方法，降维技术，类加权方案，以及扩展了用作软投票方法输入的超参数组合的数量。我们在每种解决方案上都取得了有限的成功。结论我们得出结论，基于细胞系中RNA的表达水平，有效地预测患者的DILI将需要其他方法和/或数据集。审阅者本文由PawełP Labaj和Aleksandra Gruca（均由David P Kreil提名）审阅。