Drug sensitivity prediction is one of the critical tasks involved in drug designing and discovery. Recently several online databases and consortiums have contributed to providing open access to pharmacogenomic data. These databases have helped in developing computational approaches for drug sensitivity prediction. Cancer is a complex disease involving the heterogeneous behaviour of same tumour-type patients towards the same kind of drug therapy. Several methods have been proposed in the literature to predict drug sensitivity. However, these methods are not efficient enough to predict drug sensitivity. The present study has proposed an ensemble learning framework for drug-response prediction using a modified rotation forest. The proposed framework is further compared with three state-of-the-art algorithms and two baseline methods using Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) drug screens. The authors have also predicted missing drug response values in the data set using the proposed approach. The proposed approach outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.14 and 0.404 is achieved using GDSC and CCLE drug screens, respectively. The obtained results show that the proposed framework has considerable potential to improve anti-cancer drug response prediction.

中文翻译：

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用于药物敏感性预测的集成机器学习框架。

药物敏感性预测是药物设计和发现中涉及的关键任务之一。最近，一些在线数据库和联盟为提供对药物基因组数据的开放访问做出了贡献。这些数据库有助于开发药物敏感性预测的计算方法。癌症是一种复杂的疾病，涉及相同肿瘤类型患者对相同药物治疗的异质行为。文献中提出了几种预测药物敏感性的方法。然而，这些方法不足以有效地预测药物敏感性。本研究提出了一种使用改进的旋转森林进行药物反应预测的集成学习框架。所提出的框架进一步与三种最先进的算法和两种使用癌症药物敏感性基因组学 (GDSC) 和癌细胞系百科全书 (CCLE) 药物筛选的基线方法进行比较。作者还使用所提出的方法预测了数据集中缺失的药物反应值。即使在设计该方法时没有纳入基因突变数据，所提出的方法也优于其他同类方法。使用 GDSC 和 CCLE 药物筛选分别实现了 3.14 和 0.404 的平均均方误差。获得的结果表明，所提出的框架在改善抗癌药物反应预测方面具有相当大的潜力。作者还使用所提出的方法预测了数据集中缺失的药物反应值。即使在设计该方法时没有纳入基因突变数据，所提出的方法也优于其他同类方法。使用 GDSC 和 CCLE 药物筛选分别实现了 3.14 和 0.404 的平均均方误差。获得的结果表明，所提出的框架在改善抗癌药物反应预测方面具有相当大的潜力。作者还使用所提出的方法预测了数据集中缺失的药物反应值。即使在设计该方法时没有纳入基因突变数据，所提出的方法也优于其他同类方法。使用 GDSC 和 CCLE 药物筛选分别实现了 3.14 和 0.404 的平均均方误差。获得的结果表明，所提出的框架在改善抗癌药物反应预测方面具有相当大的潜力。