A team at the University of California, Irvine developed SIGNET, a machine‑learning framework that infers cell‑type–specific gene regulatory networks (GRNs) and applied it to Alzheimer’s disease samples to produce what the authors describe as the first cell‑type GRN map for AD. The study, led by Min Zhang, MD, PhD, and Dabao Zhang, PhD, was published in Alzheimer’s & Dementia and identifies influential hub genes and pathways implicated in memory loss and neurodegeneration. SIGNET emphasizes causal regulatory relationships rather than simple correlations, enabling the prioritization of transcription factors and target genes by putative directional influence. The researchers highlight candidate biomarkers and therapeutic targets for early detection and intervention. A gene regulatory network (GRN) denotes interactions where transcription factors control gene expression; SIGNET attempts to infer causal edges within those networks from single‑cell and bulk data.