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Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization

机译:使用加权图规则化矩阵分解的细胞系中的抗癌药物反应预测

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摘要

Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines. Keywords: drug response, cell line, graph regularization, matrix factorization, response prediction
机译:精密医学已成为一种新颖且升高的概念,这取决于鉴定不同患者的个体基因组特征。癌细胞系可以反映原发性肿瘤的“OMIC”多样性,基于在实验和计算方面研究癌症生物学和药物发现的许多作品。在这项工作中,我们介绍了一种利用加权图规则化矩阵分解(WGRMF)的新方法,以推断细胞系中的抗癌药物反应。我们构建了一个p最接近的邻图,以分别稀疏药物相似性矩阵和细胞线相似矩阵。在图形正则化术语中使用稀疏的矩阵,我们执行了矩阵分解,以产生药物和细胞系的潜在矩阵。包括邻居信息的图形正规术语可以有助于排除嘈杂的成分并提高预测准确性。计算了10倍的交叉验证,并计算了Pearson相关系数(PCC),根均方误差(RMSE),PCCSR和RMSSR对所有药物的平均值,以评估WGRMF的性能。对于PCC,RMSE,PCCSR和RMSSR,癌症(GDSC)数据集的药物敏感性基因组学的结果为0.64±0.16,1.37±0.35,0.73±0.44,1.71±0.44。对于癌细胞系(CCL)数据集,WGRMF分别得到0.72±0.09,0.56±0.19,0.79±0.07和0.69±0.19的结果。结果表明,与先前的方法相比,WGRMF的优越性。此外,基于使用GDSC数据集的预测结果,进行了三种类型的案例研究。交叉验证和案例研究的结果表明了WGRMF对细胞系中药物反应预测的有效性。关键词:药物反应,细胞系,图规范化,矩阵分解,响应预测

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