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ExAD-GNN: Explainable Graph Neural Network for Alzheimer's Disease State Prediction from Single-cell Data.

Ziheng Duan¹, Cheyu Lee¹, Jing Zhang¹

¹Department of Computer Science, University of California, Irvine, USA.

APSIPA Transactions on Signal and Information Processing

|January 26, 2026

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This study introduces ExAD-GNN, a novel AI tool that predicts Alzheimer's disease (AD) from single-cell data. It offers molecular insights and identifies key genes, improving early AD diagnosis accuracy.

Area of Science:

Computational biology
Neuroscience
Genomics

Background:

Alzheimer's disease (AD) diagnosis requires early detection for effective treatment.
Current machine learning methods for AD diagnosis lack molecular detail and ignore brain heterogeneity.
This limits understanding of complex disease mechanisms.

Purpose of the Study:

To develop an Explainable Graph Neural Network (ExAD-GNN) for predicting AD from single-cell sequencing data.
To achieve cellular-level AD pathology prediction and identify cell-type-specific AD marker genes.
To provide molecular insights into AD pathology using interpretable AI.

Main Methods:

Utilized K Nearest Neighbours (KNN) graphs from single-cell expression profiles.
Developed ExAD-GNN, an Explainable Graph Neural Network model.

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Generating Neural Retina from Human Pluripotent Stem Cells

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Employed a learnable gene importance metric for marker gene identification.

Main Results:

ExAD-GNN demonstrated improved accuracy and robustness in AD prediction across diverse cell types and samples compared to state-of-the-art methods.
The model successfully identified key AD risk genes, validated by literature.
The explainability scheme effectively highlighted important biological markers.

Conclusions:

ExAD-GNN offers a powerful, interpretable approach for AD diagnosis using single-cell RNA sequencing data.
The tool enhances understanding of AD at a molecular and cellular level.
ExAD-GNN is publicly available to aid scientific research in neurodegenerative disorders.