Graph neural network maps Alzheimer's phenotypes from single-cell data
AI-based characterization of Alzheimer’s disease phenotypes from population-scale single-cell data
The method shows how graph neural networks can extract clinically meaningful subtypes from population-scale single-cell data, turning donor tissue into a map of resilience and psychiatric comorbidity in AD. Researchers in computational biology and drug discovery can reuse the approach on other diseases where donor cohorts exist. Teams building biomarker pipelines should note the link between cell-level patterns and depression onset in AD patients.
A study published in Nature Medicine on 23 September 2026 applies a graph neural network to single-cell data from 584 brain donors. The cohort included patients with Alzheimer's disease (AD) and controls. The model identifies differential patterns between disease and control tissue. It also pinpoints cell-level signatures linked to cognitive resilience and to the onset of depression in AD patients.
AI-based characterization of Alzheimer’s disease phenotypes from population-scale single-cell data
Nature Medicine, Published online: 23 September 2026; doi:10.1038/s41591-025-04128-1 Based on single-cell data from a cohort of 584 brain donors including patients with Alzheimer’s disease (AD) and controls, a graph neural network is used to investigate differential patterns between disease and control, to identify those related to cognitive resilience and to insurgence of depression in patients with AD.
