Q-Rank: Reinforcement Learning for Recommending Algorithms to Predict Drug Sensitivity to Cancer Therapy
Q-Rank: Reinforcement Learning for Recommending Algorithms to Predict Drug Sensitivity to Cancer Therapy
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DOI：
10.1109/jbhi.2020.3004663
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发表时间：
2020-11-01
影响因子：
7.7
通讯作者：
Freisleben, Bernd
中科院分区：
文献类型：
--
作者：
Daoud, Salma;Mdhaffar, Afef;Freisleben, Bernd
在个性化医疗中，一项具有挑战性的任务是确定对患者最有效的治疗方法。在肿瘤学中，已经开发了几个计算模型来预测药物对治疗的反应。然而，这些模型的表现取决于多种因素。本文提出了一种称为Q-Rank的新方法来预测细胞系对抗癌药物的敏感性。Q-Rank集成了不同的预测算法，并为给定的应用确定了合适的算法。Q-Rank基于强化学习方法，根据相关特征(例如，组学特征)对预测算法进行排名。推荐使用最优排序算法来预测药物对治疗的反应。我们的实验结果表明，Q-Rank在预测细胞系对不同药物的敏感性方面优于集成模型。
In personalized medicine, a challenging task is to identify the most effective treatment for a patient. In oncology, several computational models have been developed to predict the response of drugs to therapy. However, the performance of these models depends on multiple factors. This paper presents a new approach, called Q-Rank, to predict the sensitivity of cell lines to anti-cancer drugs. Q-Rank integrates different prediction algorithms and identifies a suitable algorithm for a given application. Q-Rank is based on reinforcement learning methods to rank prediction algorithms on the basis of relevant features (e.g., omics characterization). The best-ranked algorithm is recommended and used to predict the response of drugs to therapy. Our experimental results indicate that Q-Rank outperforms the integrated models in predicting the sensitivity of cell lines to different drugs.