Gene Level GNN

Gene-level Graph Neural Networks (GNNs) leverage the power of graph representations to model relationships between genes and other biological entities, aiming to improve the accuracy and interpretability of biological data analysis. Current research focuses on enhancing GNN expressivity, addressing challenges like oversmoothing and adversarial attacks, and developing efficient training methods for large-scale datasets, often incorporating techniques from reinforcement learning and knowledge distillation. These advancements hold significant promise for accelerating drug discovery, improving disease diagnosis, and furthering our understanding of complex biological systems.