IEEE/ACM Trans Comput Biol Bioinform. 2020 Jan-Feb;17(1):110-123. doi: 10.1109/TCBB.2018.2848908. Epub 2018 Jun 25.
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.
为合适的患者选择合适的药物是精准医学的主要目标。在本文中，我们在学习排序框架中考虑癌症药物选择的问题。我们将癌症药物选择问题表述为基于癌症药物对癌细胞系的反应，准确预测 1）敏感药物的排序位置和 2）敏感药物之间的排序顺序。我们开发了一种新的学习排序方法，记为 pLETORg，通过使用药物潜在向量和细胞系潜在向量来预测每个细胞系中的药物排序结构。pLETORg 方法通过显式地强制在每个细胞系的药物排序列表中，将敏感药物推到不敏感药物之上，同时正确排列敏感药物之间的顺序，来学习潜在向量。在学习潜在向量时利用了细胞系的基因组学信息。我们在基准细胞系-药物反应数据集上的实验结果表明，新的 pLETORg 方法在优先考虑新的敏感药物方面明显优于最先进的方法。
