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Q-Rank: Reinforcement Learning for Recommending Algorithms to Predict Drug Sensitivity to Cancer Therapy.

Abstract：

: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.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

Daoud S,Mdhaffar A,Jmaiel M,Freisleben B

doi

10.1109/JBHI.2020.3004663

subject

Has Abstract

pub_date

2020-11-01 00:00:00

pages

3154-3161

issue

11

eissn

2168-2194

issn

2168-2208

journal_volume

24

pub_type

杂志文章

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