A University of California, Irvine-led team developed SIGNET, a machine-learning framework that infers cell-type-specific gene regulatory networks for Alzheimer’s disease and produced what the authors describe as the first cell-type GRN map for AD. Using SIGNET, the investigators identified influential hub genes and pathways that may drive neurodegeneration and memory loss, reporting their findings in Alzheimer’s & Dementia. The method prioritizes causal interactions over correlations, offering targets for early detection and therapeutic intervention. Team leads Min Zhang and Dabao Zhang emphasized the framework’s applicability beyond AD to other complex diseases where cell-type resolution of regulatory mechanisms is essential.