  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.
