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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines
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DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines

机译：Deepdsc：一种预测癌细胞系药物敏感性的深度学习方法

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High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifying molecular genomic determinants of drug sensitivity and developing new anticancer drugs. In this study, we have developed a deep learning architecture to improve the performance of drug sensitivity prediction based on these data. We integrated both genomic features of cell lines and chemical information of compounds to predict the half maximal inhibitory concentrations (IC50) on the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC) datasets using a deep neural network, which we called DeepDSC. Specifically, we first applied a stacked deep autoencoder to extract genomic features of cell lines from gene expression data, and then combined the compounds' chemical features to these genomic features to produce final response data. We conducted 10-fold cross-validation to demonstrate the performance of our deep model in terms of root-mean-square error (RMSE) and coefficient of determination R-2. We show that our model outperforms the previous approaches with RMSE of 0.23 and R-2 of 0.78 on CCLE dataset, and RMSE of 0.52 and R-2 of 0.78 on GDSC dataset, respectively. Moreover, to demonstrate the prediction ability of our models on novel cell lines or novel compounds, we left cell lines originating from the same tissue and each compound out as the test sets, respectively, and the rest as training sets. The performance was comparable to other methods.
机译：高通量筛选技术为癌细胞系和数百种化合物提供了大量的药物敏感性数据。分析这些数据的计算方法可以通过鉴定药物敏感性和开发新的抗癌药物的分子基因组目的来利用抗癌治疗剂。在这项研究中，我们开发了一种基于这些数据的吸毒灵敏度预测性能的深度学习架构。我们综合了细胞系的基因组特征和化合物的化学信息，以预测癌细胞系对癌细胞系（CCL）的半最大抑制浓度（IC50）以及使用深神经网络的癌症（GDSC）数据集的药物敏感性的基因组学，我们称之为Deepdsc。具体而言，我们首先应用堆叠的深度自身阳极，以从基因表达数据中提取细胞系的基因组特征，然后将化合物的化学特征与这些基因组特征组合以产生最终的响应数据。我们进行了10倍的交叉验证，以展示我们深层模型的性能，以便在根均方误差（RMSE）和确定系数R-2的方面。我们表明，我们的模型优于先前的方法0.23和R-2在CCL数据集上的0.23和R-2的方法，并分别在GDSC数据集上的0.52和R-2的0.52和R-2的RMSE。此外，为了证明我们在新型细胞系或新化合物上的模型的预测能力，我们留下源自同一组织的细胞系和每个化合物，分别作为测试集，其余作为训练集。性能与其他方法相当。

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