A University of California, Irvine team published what they describe as the first cell‑type‑specific gene regulatory network (GRN) map for Alzheimer’s disease, using a machine‑learning framework called SIGNET to infer causal relationships among genes across brain cell types. The work, reported in Alzheimer's & Dementia, identifies hub genes and pathways that could serve as biomarkers or therapeutic targets and claims applicability to other complex diseases. The study authors—Min Zhang, MD, PhD, and Dabao Zhang, PhD—say SIGNET moves beyond correlation to causal inference in single‑cell data, offering a new resource for target discovery and early detection efforts in neurodegeneration.