A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.

中文翻译：

---

预测癌症中药物反应的机器学习和数据挖掘框架：概述和基于关联规则挖掘的新型计算机筛选过程。

癌症治疗中的主要挑战是个性化地预测对抗癌药物的临床反应。这项任务的成功很大程度上取决于开发将大型“组学”数据整合到有效药物反应模型中的计算资源的能力。机器学习既是一个扩展的领域，也是一个不断发展的计算领域，它有望满足此类需求。在这里，我们提供以下内容的重点概述：1）专门用于药物反应预测应用的各种监督和非监督算法； 2）将这些算法开发为适用模型所采用的策略； 3）被馈送到这些框架中的数据资源； 4）最大化模型性能的陷阱和挑战。在这种情况下，我们还描述了一种基于关联规则挖掘的新颖的计算机筛选程序，用于识别基因作为药物反应的候选驱动因素，并将其与相关的数据挖掘框架进行比较，为此我们生成了一个免费的Web应用程序：https：//compbio.nyumc.org/drugs/。该管道可以高效地探索大型样本空间，同时甚至可以在多维空间中检测低频事件并评估统计显着性，并以易于解释的规则的形式呈现结果。我们总结了在精密医学中应用基于机器学习的药物反应预测的未来前景和挑战。同时即使在多维空间中也能够检测低频事件并评估统计显着性，并以易于解释的规则的形式呈现结果。我们总结了在精密医学中应用基于机器学习的药物反应预测的未来前景和挑战。同时即使在多维空间中也能够检测低频事件并评估统计显着性，并以易于解释的规则的形式呈现结果。我们总结了在精密医学中应用基于机器学习的药物反应预测的未来前景和挑战。