Title: Drug Selection via Joint Push and Learning to Rank,
Abstract: Selecting the right drugs for the right patients is a primary goal of
precision medicine. In this manuscript, 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.