Drug Response Prediction Based on 1D Convolutional Neural Network and Attention Mechanism.

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.

Application of epidermal growth factor in lung cancer treatment / 国际肿瘤学杂志

Epidermal growth factor(EGF)and epidermal growth factor receptor (ECFR) signal pathways play important roles in initiation and advancement of non- small cell lung cancer (NSCLC). We review general characteristics, mechanism and biological effects of EGF and EGFR. Targeted treatment effecfs of tyro-sine kinase inhibitor( TKI) and EGF vaccine in NSCLC are also discussed.