Prediction of anti-cancer drug response by kernelized multi-task learning

作者：

Highlights：

• We proposed to use kernelized multitask learning for anticancer drug activity prediction.

• The proposed method was found to outperform the previous methods in terms of cytotoxicity prediction on three different data sets.

• The method not only performs better but also requires few parameters.

• New drugs predicted by the method to be active against certain cell lines were listed.

摘要

Highlights•We proposed to use kernelized multitask learning for anticancer drug activity prediction.•The proposed method was found to outperform the previous methods in terms of cytotoxicity prediction on three different data sets.•The method not only performs better but also requires few parameters.•New drugs predicted by the method to be active against certain cell lines were listed.

论文关键词：Multi-task learning,Drug response prediction,Cancer cell lines,Gene expression data

论文评审过程：Received 18 May 2016, Revised 10 August 2016, Accepted 29 September 2016, Available online 3 October 2016, Version of Record 13 October 2016.

论文官网地址：https://doi.org/10.1016/j.artmed.2016.09.004