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DeepCDR: a hybrid graph convolutional network for predicting cancer drug response
Bioinformatics ( IF 4.4 ) Pub Date : 2020-12-29 , DOI: 10.1093/bioinformatics/btaa822 Qiao Liu 1, 2 , Zhiqiang Hu 2, 3 , Rui Jiang 1, 2 , Mu Zhou 4
Bioinformatics ( IF 4.4 ) Pub Date : 2020-12-29 , DOI: 10.1093/bioinformatics/btaa822 Qiao Liu 1, 2 , Zhiqiang Hu 2, 3 , Rui Jiang 1, 2 , Mu Zhou 4
Affiliation
Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology.
中文翻译:
DeepCDR:预测癌症药物反应的混合图卷积网络
由于癌症患者的药物疗效和异质性尚不确定,因此准确预测癌症药物反应(CDR)具有挑战性。有力的证据表明CDR对个别患者的肿瘤基因组和转录组谱具有高度依赖性。CDR的精确鉴定对于指导抗癌药物设计和理解癌症生物学都至关重要。
更新日期:2020-12-31
中文翻译:
DeepCDR:预测癌症药物反应的混合图卷积网络
由于癌症患者的药物疗效和异质性尚不确定,因此准确预测癌症药物反应(CDR)具有挑战性。有力的证据表明CDR对个别患者的肿瘤基因组和转录组谱具有高度依赖性。CDR的精确鉴定对于指导抗癌药物设计和理解癌症生物学都至关重要。