Researchers at Columbia University and the Chan Zuckerberg Initiative unveiled GREmLN, a novel graph-based artificial intelligence model designed to capture complex gene regulatory networks from single-cell RNA sequencing data. GREmLN outperformed existing models in cell type annotation and understanding gene interactions by incorporating biological causality rather than relying on sequence-based logic. The model facilitates improved predictions of cellular behavior relevant for diseases and therapeutic targeting, advancing efforts to build accurate virtual human cell models.