Biological Network

Biological networks represent complex systems of interacting entities, such as genes, proteins, or drugs, with research focused on understanding their structure, dynamics, and function. Current investigations utilize various graph neural network architectures, including graph convolutional networks and hypergraph neural networks, along with novel algorithms like counter-current learning and Bayesian inference methods, to model and predict interactions within these networks. This research is crucial for advancing fields like drug repurposing, precision medicine, and understanding disease mechanisms, enabling more effective interventions and personalized treatments. The development of scalable and interpretable models is a key focus to overcome challenges posed by large, heterogeneous datasets and complex network topologies.