论文标题
深贝叶斯匪徒的抗癌治疗方法：通过功能性探索
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior
论文作者
论文摘要
通过机器学习学习个性化的癌症治疗具有巨大的希望，可以改善癌症患者的生存机会。尽管机器学习和精确肿瘤学取得了最新进展，但这种方法仍然具有挑战性，因为在临床前/临床研究中收集数据以建模多种治疗效率通常是一个昂贵的，耗时的过程。此外，由于某些参与者/样本在试验期间未接受最合适的治疗方法，因此治疗分配的随机分配被证明是次优的。为了应对这一挑战，我们将药物筛查研究作为“上下文匪徒”问题，其中算法根据有关癌细胞系的上下文信息选择抗癌治疗剂，同时调整其治疗策略以最大程度地以“在线”方式以最大化治疗反应。我们建议使用一种新型的深贝叶斯土匪框架，该框架在近似后验之前使用功能，以基于由基因组特征和药物结构组成的多模式信息进行药物反应预测。我们对三个大规模的体外药物基因组学数据集进行了经验评估我们的方法，并表明我们的方法在识别给定细胞系的最佳处理方面优于几个基准。
Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging as collecting data in preclinical/clinical studies for modeling multiple treatment efficacies is often an expensive, time-consuming process. Moreover, the randomization in treatment allocation proves to be suboptimal since some participants/samples are not receiving the most appropriate treatments during the trial. To address this challenge, we formulate drug screening study as a "contextual bandit" problem, in which an algorithm selects anticancer therapeutics based on contextual information about cancer cell lines while adapting its treatment strategy to maximize treatment response in an "online" fashion. We propose using a novel deep Bayesian bandits framework that uses functional prior to approximate posterior for drug response prediction based on multi-modal information consisting of genomic features and drug structure. We empirically evaluate our method on three large-scale in vitro pharmacogenomic datasets and show that our approach outperforms several benchmarks in identifying optimal treatment for a given cell line.
