Application of geometric deep learning in disease-gene relationship prediction

The correlation between diseases and genetic factors represents a pivotal challenge in the biomedical field, often conceptualized as a task of link prediction. Conventional methods employed for prediction, such as statistical models and various machine learning algorithms, frequently fall short in terms of accuracy when confronted with the intricacies of biological network data. These methods also tend to inadequately represent the complex relationships inherent in such data. In contrast, recent advances in geometric deep learning have introduced a powerful tool within artificial intelligence, particularly adept at processing non-Euclidean data structures. This study delves into the potential of leveraging geometric deep learning techniques to enhance the prediction of disease-gene associations. Initially, we conduct a thorough review of existing research related to link prediction, encompassing both traditional approaches and contemporary methods grounded in deep learning. Subsequently, we propose a geometric deep learning framework, incorporating Graph Convolutional Networks (GCN) and Graph Auto-Encoder (GAE), to develop and assess our predictive model. The results of our experiments demonstrate that the proposed geometric deep learning model surpasses conventional techniques in accurately predicting disease-gene associations. In conclusion, we evaluate the implications of our findings, discuss their practical applications in the biomedical domain, and suggest possible avenues for future research.