ExAD-GNN: Explainable Graph Neural Network for Alzheimer’s Disease State Prediction from Single-cell Data
Ziheng Duan, Cheyu Lee, Jing Zhang · 2023
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Description
Ziheng Duan, Cheyu Lee, and Jing Zhang present a 2023 paper on predicting Alzheimer's disease from single-cell data. Early, accurate diagnosis could improve care, but most machine learning tools ignore the variety of cells in the human brain. They also give little insight into which genes drive the disease.
Their model, ExAD-GNN, is a graph neural network that can explain its calls. It links each cell to its nearest neighbors by gene activity, then predicts disease state cell by cell. A learnable score ranks genes by importance for each cell type. On large single-cell data sets the model beat rival methods in accuracy and held up across samples. Its top risk genes matched much of the published record.
Catalog record
| CREATOR | Ziheng Duan, Cheyu Lee, Jing Zhang |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |