Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Swiss Institute of Bioinformatics, Basel, Switzerland.
Bioinformatics. 2018 Aug 15;34(16):2808-2816. doi: 10.1093/bioinformatics/bty132.
Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (i) medical records only contain the response of a patient to very few drugs, (ii) drugs are recommended by doctors based on their expert judgment and (iii) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties.
We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic.
The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning.
Supplementary data are available at Bioinformatics online.
为了更好地了解药物反应的遗传成分，并提高根据患者分子谱推荐疗法的能力，目前正在对具有详细分子谱的癌细胞系进行大规模筛选，并与药理学化合物文库进行比较。这些全面的筛选与临床环境不同，（i）病历仅包含患者对极少数药物的反应，（ii）药物是根据医生的专业判断推荐的，（iii）选择最有前途的治疗方法通常比准确预测对所有潜在药物的敏感性更为重要。目前用于药物敏感性预测的回归模型未能考虑到这三个特性。
我们提出了一种名为核化秩学习（KRL）的机器学习方法，该方法根据药物对每个细胞系（患者）的预测效果对药物进行排序，从而避免了精确预测对给定药物的敏感性这一难题。与几种最先进的预测器相比，我们的方法在药物推荐方面表现出色，特别是在训练数据集稀疏且可推广到患者数据的情况下。我们的工作将个性化药物推荐表述为一种具有转化潜力的新型机器学习问题，可以应用于临床。
KRL 的 Python 实现和运行我们实验的脚本可在 https://github.com/BorgwardtLab/Kernelized-Rank-Learning 上获得。
补充数据可在生物信息学在线获得。
