Researchers at University of California, Irvine developed SIGNET, a machine‑learning framework that infers causal gene regulatory networks (GRNs) across specific brain cell types and used it to build what they call the first cell‑type‑specific GRN map for Alzheimer’s disease. The study, led by Min Zhang, MD, PhD, and Dabao Zhang, PhD, identifies hub genes and pathways potentially driving neurodegeneration and offers targets for early detection and intervention. Authors published the work in Alzheimer’s & Dementia and argue that SIGNET moves beyond correlation to infer causal regulatory interactions between transcription factors and genes within neurons, microglia, and other brain cells. They highlight hub genes that could be pursued as biomarkers or therapeutic entry points. Why it matters: cell-type-resolved causal maps enable more precise target nomination in a field where tissue heterogeneity has confounded drug discovery and biomarker development.