作者
Joseph D Janizek, Safiye Celik, Su-In Lee
发表日期
2018/1/1
研讨会论文
ICML Workshop on Computational Biology
页码范围
331769
出版商
Cold Spring Harbor Laboratory
简介
Although combination therapy has been a mainstay of cancer treatment for decades, it remains challenging, both to identify novel effective combinations of drugs and to determine the optimal combination for a particular patient’s tumor. While there have been several recent efforts to test drug combinations in vitro, examining the immense space of possible combinations is far from being feasible. Thus, it is crucial to develop datadriven techniques to computationally identify the optimal drug combination for a patient. We introduce TreeCombo, an extreme gradient boosted tree-based approach to predict synergy of novel drug combinations, using chemical and physical properties of drugs and gene expression levels of cell lines as features. We find that TreeCombo significantly outperforms three other state-of-theart approaches, including the recently developed DeepSynergy, which uses the same set of features to predict synergy using deep neural networks. Moreover, we found that the predictions from our approach were interpretable, with genes having well-established links to cancer serving as important features for prediction of drug synergy.
学术搜索中的文章
JD Janizek, S Celik, SI Lee - BioRxiv, 2018