Previous studies have either learned drug's features from their string or numeric representations, which are not natural forms of drugs, or only used genomic data of cell lines for the drug response prediction problem. Here, we proposed a deep learning model, GraOmicDRP, to learn drug's features from their graph representation and integrate multiple -omic data of cell lines. In GraOmicDRP, drugs are represented as graphs of bindings among atoms; meanwhile, cell lines are depicted by not only genomic but also transcriptomic and epigenomic data. Graph convolutional and convolutional neural networks were used to learn the representation of drugs and cell lines, respectively. A combination of the two representations was then used to be representative of each pair of drug-cell line. Finally, the response value of each pair was predicted by a fully connected network. Experimental results indicate that transcriptomic data shows the best among single -omic data; meanwhile, the combinations of transcriptomic and other –omic data achieved the best performance overall in terms of both Root Mean Square Error and Pearson correlation coefficient. In addition, we also show that GraOmicDRP outperforms some state-of-the-art methods, including ones integrating –omic data with drug information such as GraphDRP, and ones using –omic data without drug information such as DeepDR and MOLI.

中文翻译：

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整合药物的分子图数据和细胞系的多组学数据以进行药物反应预测


以前的研究要么从字符串或数字表示中了解药物的特征，这不是药物的自然形式，要么仅使用细胞系的基因组数据来解决药物反应预测问题。在这里，我们提出了一种深度学习模型 GraOmicDRP，从图形表示中学习药物的特征并整合细胞系的多个组学数据。在 GraOmicDRP 中，药物被表示为原子之间的结合图；同时，细胞系不仅通过基因组数据来描述，还通过转录组和表观基因组数据来描述。图卷积和卷积神经网络分别用于学习药物和细胞系的表示。然后使用两种表示的组合来代表每对药物细胞系。最后，通过全连接网络预测每对的响应值。实验结果表明，单组学数据中转录组数据表现最好；同时，转录组学和其他组学数据的组合在均方根误差和皮尔逊相关系数方面均取得了总体最佳性能。此外，我们还表明 GraOmicDRP 优于一些最先进的方法，包括将 –omic 数据与药物信息集成的方法（例如 GraphDRP），以及使用不带药物信息的 –omic 数据的方法（例如 DeepDR 和 MOLI）。