Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, 44106, USA.
Department of Electrical Engineering and Computer Science, Case School of Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
Sci Rep. 2017 Jan 9;7:40321. doi: 10.1038/srep40321.
Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute "network profiles" for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.
药物反应预测是一个研究得很好的问题，其中给定样本的分子谱用于预测给定药物对该样本的影响。这个问题的有效解决方案是精准医学的关键。在癌症研究中，细胞系的基因组数据通常被用作特征来开发预测药物反应的机器学习模型。分子网络为整合基因组特征提供了一个功能背景，从而产生了稳健且可重复的预测模型。然而，网络数据的包含增加了维度，并对常见的机器学习任务提出了额外的挑战。为了克服这些挑战，我们将药物反应预测表述为链接预测问题。为此，我们将大量细胞系的药物反应数据表示为一个异构网络。使用这个网络，我们为细胞系和药物计算“网络特征”。然后，我们使用这些特征之间的关联来预测药物和细胞系之间的链接。通过在独立数据集上进行留一交叉验证和交叉分类，我们表明这种方法可以准确且可重复地对敏感和耐药细胞系-药物对进行分类，准确率为 85%。我们还研究了网络特征的生物学相关性。
