Study of Deep and Convolutional Neural Networks for Personalized Chemotherapy Drug Response Prediction

Due to the significant differences between cancer patients, traditional chemotherapy regimens often vary in efficacy and side effects, making it difficult to meet the needs of precision treatment. The rise of deep learning has provided new ideas for drug response prediction and has gradually become a hot topic in personalized treatment research. This review focuses on the application progress of deep neural networks and convolutional neural networks in personalized cancer chemotherapy drug prediction and systematically sorts out the data types, modeling methods, and prediction performances involved in existing studies. This article evaluates the application results of Deep Neural Network (DNN), Convolutional Neural Network (CNN), and their fusion models in cancer drug sensitivity prediction from the perspectives of multi-omics data integration, gene expression image conversion, and time series modeling. By comparing the advantages and disadvantages of various models in processing high-dimensional data, feature extraction capabilities, and generalization performance, this article summarizes the applicable scenarios and limitations of current methods. Studies have shown that DNN has outstanding advantages in multi-omics fusion and complex relationship modeling, CNN is suitable for local feature extraction and image-based gene data analysis, and the Recurrent Neural Network - Convolutional Neural Network (RNN-CNN) model improves the accuracy of time series data modeling. Finally, this article discusses the challenges of model interpretability, data standardization, and clinical transformation and proposes future development directions.