A University of California, Irvine team introduced SIGNET, a machine‑learning framework that infers cell‑type‑specific gene regulatory networks in Alzheimer’s disease and identifies causal 'hub' genes and pathways. The study, published in Alzheimer’s & Dementia, moves beyond correlation to suggest directional regulatory relationships across neuronal and glial cell types. Authors Min Zhang and Dabao Zhang reported new candidate biomarkers and targets for early detection and intervention. SIGNET’s causal-inference approach may be applicable across complex diseases, enabling more targetable hypotheses for translational programs in neurodegeneration and oncology. For drug developers, the work supplies prioritized cell‑type targets and a methodological roadmap to translate single‑cell data into mechanistic targets.