Researchers at UC Irvine developed SIGNET, a machine‑learning framework to infer causal gene regulatory networks at cell‑type resolution and applied it to Alzheimer’s disease. The team identified novel hub genes and pathways that may drive neurodegeneration, publishing findings in Alzheimer’s & Dementia. SIGNET moves beyond correlation to propose causal TF–gene relationships within specific brain cell types, producing a resource that could prioritize biomarkers and therapeutic targets. Authors suggested the method is extensible to other complex diseases and could inform early‑detection strategies and mechanistic studies. The work offers a new computational lens for target discovery in Alzheimer’s, where cell‑type specificity and causal inference have been major bottlenecks for translating genetic signals into interventions.