School of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Viet Nam.
Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
J Mol Biol. 2018 Sep 14;430(18 Pt A):2993-3004. doi: 10.1016/j.jmb.2018.06.041. Epub 2018 Jun 30.
One of the most important problem in personalized medicine research is to precisely predict the drug response for each patient. Due to relationships between drugs, recent machine learning-based methods have solved this problem using multi-task learning models. However, chemical relationships between drugs have not been considered. In addition, using very high dimensions of -omics data (e.g., genetic variant and gene expression) also limits the prediction power. A recent dual-layer network-based method was proposed to overcome these limitations by embedding gene expression features into a cell line similarity network and drug relationships in a chemical structure-based drug similarity network. However, this method only considered neighbors of a query drug and a cell line. Previous studies also reported that genetic variants are less informative to predict an outcome than gene expression. Here, we develop a novel network-based method, named GloNetDRP, to overcome these limitations. Besides gene expression, we used the genetic variant to build another cell line similarity network. First, we constructed a heterogeneous network of drugs and cell lines by connecting a drug similarity network and a cell line similarity network by known drug-cell line responses. Then, we proposed a method to predict the responses by exploiting not only the neighbors but also other drugs and cell lines in the heterogeneous network. Experimental results on two large-scale cell line data sets show that prediction performance of GloNetDRP on gene expression and genetic variant data is comparable. In addition, GloNetDRP outperformed dual-layer network- and typical multi-task learning-based methods.
个性化医学研究中最重要的问题之一是如何准确预测每个患者的药物反应。由于药物之间的关系，最近基于机器学习的方法使用多任务学习模型解决了这个问题。然而，药物之间的化学关系尚未得到考虑。此外，使用 -omics 数据（例如遗传变异和基因表达）的非常高维也限制了预测能力。最近提出了一种基于双层网络的方法，通过将基因表达特征嵌入细胞系相似性网络和基于化学结构的药物相似性网络中的药物关系来克服这些限制。然而，该方法仅考虑了查询药物和细胞系的邻居。先前的研究还报告说，遗传变异对预测结果的信息量不如基因表达。在这里，我们开发了一种名为 GloNetDRP 的新型基于网络的方法来克服这些限制。除了基因表达，我们还使用遗传变异来构建另一个细胞系相似性网络。首先，我们通过连接药物相似性网络和细胞系相似性网络来构建药物和细胞系的异构网络，以已知的药物-细胞系反应。然后，我们提出了一种通过利用异构网络中的不仅是邻居，还有其他药物和细胞系来预测反应的方法。在两个大规模细胞系数据集上的实验结果表明，GloNetDRP 在基因表达和遗传变异数据上的预测性能相当。此外，GloNetDRP 优于双层网络和典型的多任务学习方法。
