## 2 Review of Existing Work
These genes are closely associated with biological processes such as autophagy, ErbB signaling pathway, mitochondrial autophagy, endocytosis, FoxO signaling pathway, apoptosis, and resistance to antifolate drugs. Using these candidate genes, researchers applied various machine-learning algorithms and assessed the models' performance through cross-validation methods. In other studies, machine-learning techniques have been used to predict responses to immunotherapy and neoadjuvant chemotherapy. For example, machine learning was utilized to explore the limitations of known drug mechanisms in predicting pathological complete response (pCR) and the impact of biological characteristics on response prediction in the first 10 treatment regimens of the I-SPY 2 trial [23]. Additionally, research has proposed a new classification of Hepatocellular Carcinoma (HCC) cancer stem cell-related using machine-learning algorithms based on RNA sequencing datasets to predict patients' responses to immunotherapy [24]. Deep-learning technologies have shown significant potential in precision medicine, particularly in predicting the response of bladder cancer patients to neoadjuvant therapy. These algorithms, by analyzing patterns in complex biological and chemical data, can effectively predict how cancer cell lines and patients might respond to new drugs or drug combinations, therefore aiding in personalized treatment planning [25]. For instance, deep-learning models integrating gene expression, gene mutations, and compound chemical structures have significantly improved prediction accuracy in applications on GDSC and CCLE datasets by analyzing cancer-related gene and chemical information features [26]. The CDRscan model employs a dual-step convolutional architecture to process the gene mutation fingerprint of cell lines and the molecular fingerprint of drugs, and through \"virtual docking\", it successfully identifies potential new cancer indications for existing drugs. Furthermore, models based on residual neural networks have been applied to NCI-60 drug pair screening data, focusing on drug combination activity, emphasizing the importance of drug descriptors in predicting cell line responses to drug pairings, and highlighting the potential of deep learning in predicting drug combination effects [27]. The DeepDRmodel, combining mutation encoders, expression encoders, and drug response predictors, has outperformed traditional methods in training on cancer cell lines and demonstrated potential in predicting tumor responses to drugs, providing insights into known and novel drug targets and drug resistance mechanisms [28]. These studies collectively illustrate the growing importance of deep learning in the field of cancer drug response prediction. By leveraging deep-learning technologies and integrating diverse genomic and chemical information data, these models are enhancing our understanding of drug responses and paving the way for more personalized and effective cancer treatments. However, challenges remain in the current state of research. First, to date, graph convolutional neural networks have not been extensively applied in predicting the efficacy of neoadjuvant therapy for bladder cancer, representing an unexplored avenue with potential for significant impact. Second, the inherent complexity of genes poses a considerable challenge, with their intricate interactions and variations contributing to the difficulty in accurately predicting treatment outcomes. This complexity underscores the need for advanced computational models that can effectively decipher the nuances of genomic data in the context of bladder cancer therapy.