Selecting the right drugs for the right patients is a primary goal of precision medicine. In this article, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to accurately predicting 1) the ranking positions of sensitive drugs and 2) the ranking orders among sensitive drugs in cancer cell lines based on their responses to cancer drugs. We have developed a new learning-to-rank method, denoted as pLETORg, that predicts drug ranking structures in each cell line via using drug latent vectors and cell line latent vectors. The pLETORg method learns such latent vectors through explicitly enforcing that, in the drug ranking list of each cell line, the sensitive drugs are pushed above insensitive drugs, and meanwhile the ranking orders among sensitive drugs are correct. Genomics information on cell lines is leveraged in learning the latent vectors. Our experimental results on a benchmark cell line-drug response dataset demonstrate that the new pLETORg significantly outperforms the state-of-the-art method in prioritizing new sensitive drugs.

中文翻译：

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通过联合推动和学习排名进行药物选择。

为合适的患者选择合适的药物是精密医学的主要目标。在本文中，我们在按等级学习的框架中考虑了癌症药物选择的问题。我们制定了癌症药物选择问题，以便根据对癌细胞的反应准确预测1）敏感性药物的排名位置和2）敏感性药物在癌细胞系中的排名顺序。我们已经开发出一种新的按等级排序的学习方法，称为pLETORg，它可以通过使用药物潜在载体和细胞系潜在载体来预测每个细胞系中的药物排名结构。pLETORg方法通过显式强制在每个细胞系的药物排名列表中将敏感药物推到不敏感药物之上，同时敏感药物之间的排名顺序是正确的，从而学习了这种潜在载体。细胞系的基因组学信息可用于学习潜在载体。我们在基准细胞系-药物反应数据集上的实验结果表明，在对新的敏感药物进行优先排序时，新的pLETORg明显优于最新方法。