School of Economics and Management, Changsha Normal University, Changsha, Hunan 410003, China.
School of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan 410003, China.
Comput Math Methods Med. 2022 Sep 17;2022:8671348. doi: 10.1155/2022/8671348. eCollection 2022.
There are multiple methods based on gene expression, copy number variation, and methylation biomarkers for screening drug response have been developed. On the other hand, many machine learning algorithms have been applied in recent years to predict drug response, such as neural networks and random forests for the discovery of genomic markers of drug sensitivity for individual drugs in cancer cell lines. In this paper, we propose a drug response prediction algorithm based on 1D convolutional neural networks with attention mechanism and combined with pathway networks, which combines the individual histological data affecting drug response and considers the topological nature of the pathways to find the subpathways highly correlated with drug response and use this as a feature to predict drug response by training using convolutional neural networks. Thus, the output values will represent the probability of occurrence of each of these two categories. In this experiment, using five-fold cross-validation, the identification accuracy reached an average of 84.6%, which is 4.5% higher than the direct random forest approach for drug prediction with an AUC value. This proves that the use of the one-dimensional1D convolutional neural network with attention mechanism to predict the response of low-grade glioma patients and drugs has better prediction results.
已经开发出了多种基于基因表达、拷贝数变异和甲基化生物标志物的药物反应筛选方法。另一方面，近年来许多机器学习算法已被应用于预测药物反应，例如神经网络和随机森林，用于发现癌症细胞系中个体药物的药物敏感性基因组标记。在本文中，我们提出了一种基于一维卷积神经网络与注意力机制相结合并结合途径网络的药物反应预测算法，该算法结合了影响药物反应的个体组织学数据，并考虑途径的拓扑性质，找到与药物反应高度相关的子途径，并将其用作特征，通过使用卷积神经网络进行训练来预测药物反应。因此，输出值将代表这两类情况的发生概率。在这个实验中，我们使用五重交叉验证，识别准确率平均达到了 84.6%，比直接使用随机森林方法预测药物的 AUC 值高 4.5%。这证明了使用一维卷积神经网络与注意力机制来预测低级别神经胶质瘤患者和药物的反应具有更好的预测结果。
