Cell Graph

Cell graphs represent biological systems by modeling cells as nodes and their interactions as edges, aiming to analyze complex spatial relationships and dynamics within tissues and organs. Current research focuses on developing and applying graph neural networks (GNNs), including graph convolutional networks and transformers, to analyze these graphs for tasks such as cell type classification, image segmentation, and prediction of biological processes. This approach offers improved accuracy and interpretability in analyzing large-scale biological image data, impacting fields like cancer diagnosis, drug discovery, and developmental biology by enabling more precise and efficient analysis of complex biological systems.