Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction.
Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction.
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Alexnet和Googlenet在细胞生长抑制IC50预测中的性能比较。
DOI：
10.3390/ijms22147721
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发表时间：
2021-07-19
影响因子：
5.6
通讯作者：
Nam S
中科院分区：
文献类型：
--
作者：
Lee Y;Nam S
由于基因组谱的异质性，癌症的药物反应是多种多样的。药物反应性预测对癌症治疗的临床反应具有重要意义。近年来，基于分子指纹和突变状态的深度学习（DL）模型的多类药物反应性模型应运而生。然而，对于药物反应性预测的多类模型，卷积神经网络（CNN）模型（例如AlexNet和GoogLeNet）之间的比较尚未进行。因此，在本研究中，我们比较了GoogLeNet和AlexNet两种CNN模型，并将最小绝对收缩和选择算子（LASSO）模型作为基线模型。我们以药物分子指纹图谱和细胞系突变状态为输入，构建模型，预测药物对癌细胞的高、中、低类半最大抑制浓度（IC50）值。此外，我们比较了模型在乳腺癌患者以及独立胃癌细胞系中的药物反应性数据。我们根据受试者工作特征（ROC）曲线下面积（AUROC）值来衡量模型的性能。在本研究中，我们比较了CNN模型对多类别药物反应性的预测。与LASSO相比，AlexNet和GoogLeNet表现出更好的性能。因此，DL模型在药物反应性预测方面将是精确肿瘤学的有用工具。
Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.
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实现癌症基因组数据的共同愿景。
DOI：
10.1056/nejmp1607591
发表时间：
2016-09-22
期刊：
The New England journal of medicine
影响因子：
--
作者：
Grossman RL;Heath AP;Ferretti V;Varmus HE;Lowy DR;Kibbe WA;Staudt LM
通讯作者：
Staudt LM
影响因子：
8.9
作者：
Huang, J;Ling, CX
通讯作者：
Ling, CX
影响因子：
2.7
作者：
Chiu, Yu-Chiao;Chen, Hung-I Harry;Chen, Yidong
通讯作者：
Chen, Yidong
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
8.6
作者：
Cortes-Ciriano, Isidro;Bender, Andreas
通讯作者：
Bender, Andreas