MVMSGAT: Integrating Multiview, Multi-Scale Graph Convolutional Networks with Biological Prior Knowledge for Predicting Bladder Cancer Response to Neoadjuvant Therapy  
[PERSON]  
1 Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610213, China; [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (X.C.)1  
[PERSON]  
1 Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610213, China; [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (X.C.)12 University of Chinese Academy of Sciences, Beijing 100049, China2  
[PERSON]  
1 Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610213, China; [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (X.C.)12 University of Chinese Academy of Sciences, Beijing 100049, China2  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/12/1](https://www.mdpi.com/journals/pdf/12/1)]([https://www.mdpi.com/journals/pdf/12/1](https://www.mdpi.com/journals/pdf/12/1))  
Other research has employed supervised ML methods for high-precision prediction of non-small cell lung cancer responses to immunotherapy [8]. Moreover, ML analysis of data from 762 breast cancer patients has led to the early detection of breast cancer and the identification of key biomarkers [9]. However, traditional pattern recognition methods face challenges in high-dimensional, low-sample datasets, known as the 'curse of dimensionality'.  
Incorporating biological prior knowledge into these models can enhance their generalization ability and yield better results [10]. By integrating relevant biological information, models can more accurately navigate the complex genetic data landscape, leading to clinically relevant predictions [11]. Transforming biological knowledge into graph structures is advantageous, as graphs effectively represent complex biological system relationships and interactions [12]. This method allows for a more intuitive and thorough analysis of genetic data's intricate connections. For instance, Graph Convolutional Networks (GCN) have been used to predict circular RNA-disease connections, outperforming traditional methods [13]. Another study combined GCN with biological knowledge graphs to identify genes linked to complex diseases [14]. These graph-based approaches have proven effective in enhancing model performance and stability.  
In our study, we sought to predict the effectiveness of neoadjuvant therapy in bladder cancer patients using baseline transcriptome data, multiview knowledge graphs, and graph convolutional neural network techniques. Using the Boruta algorithm, we identified significant gene sets for constructing graph nodes related to treatment outcomes. Gene co-expression and protein-protein interaction networks were formed from these nodes, enriched with extensive biological knowledge. A multi-head graph attention convolutional neural network refined features, with pooling to extract multi-scale biomarkers. An attention mechanism combined features from different network layers for outcome prediction. This method improved our model's accuracy and contributed to precision medicine. Our study's key achievements include:  
1. Creating a predictive model for bladder cancer patient responses to neoadjuvant therapy based on a multiview knowledge graph.
2. Using a multi-head graph attention mechanism to integrate gene co-expression and protein-protein interaction network features, capturing essential multi-scale features via pooling.
3. Demonstrating the model's precision in predicting treatment responses, proving its competitive advantage over existing methods.