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Comprehensive anticancer drug response prediction based on a simple cell line drug complex network model

基于简单细胞系药物复杂网络模型的综合抗癌药物反应预测

Abstract Background: Accurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Although many popular computational models have been proposed towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data. Results: We first demonstrated an observation on the CCLE and GDSC datasets, i.e., genetically similar cell lines always exhibit higher response correlations to structurally related drugs. Based on this observation we built a cell line-drug complex network model, named CDCN model. It captures different contributions of all available cell line drug responses through cell line similarities and drug similarities. We executed anticancer drug response prediction on CCLE and GDSC independently. The result is significantly superior to that of some existing studies. More importantly, our model could predict the response of new drug to new cell line with considerable performance. We also divided all possible cell lines into “ sensitive ” and “ resistant ” groups by their response values to a given drug, the prediction accuracy, sensitivity, specificity and goodness of fit are also very promising. Conclusion: CDCN model is a comprehensive tool to predict anticancer drug responses. Compared with existing methods, it is able to provide more satisfactory prediction results with less computational consumption. Keywords: Anticancer drug response, Cell line-drug complex network, Computational prediction model, Cell line, Precision medicine   摘要
背景:准确预测抗癌药物在细胞系中的反应是实现这一目标的关键步骤肿瘤学的精准医学。
尽管许多流行的计算模型已经针对这一问题提出这不是一个小问题，仍然有空间通过组合多种类型来提高预测性能
全基因组分子数据。
结果:我们首先在CCLE和GDSC数据集上展示了一个观察结果，即基因相似的细胞系
对结构相关的药物总是表现出较高的反应相关性。
基于这个观察，我们构建了一个细胞
线药复合网络模型，简称CDCN模型。
它捕获不同贡献的所有可用的细胞系药物反应通过细胞系相似性和药物相似之处。
我们进行了抗癌药物反应预测
独立使用CCLE和GDSC。
这一结果明显优于现有的一些研究。
更多的
重要的是，我们的模型可以预测新药对新细胞系的反应，具有可观的性能。
我们还将所有可能的细胞系根据它们对给定的响应值分为“敏感”和“耐药”组
药物预测的准确性、灵敏度、特异性和拟合优度也很有前景。
结论:CDCN模型是预测抗肿瘤药物反应的综合工具。相比之下,现有方法，可以用较少的计算量提供较满意的预测结果。
关键词:抗癌药物反应，细胞系-药物复合网络，计算预测模型，细胞系 精密医学

本文标签： responsepredictiondrugComprehensiveanticancer

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