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image of Attention Graphical Neural Networks-based Single-cell Multi-omics Fusion Analysis of Chromatin Accessibility and Transcriptome Characterization in Alzheimer's Disease

Abstract

Introduction

Single-cell multi-omics technologies provide a comprehensive view of cellular states and transcriptional regulatory mechanisms by integrating diverse omics data. However, their complexity and heterogeneity present significant analytical challenges, particularly in understanding neurodegenerative disorders such as Alzheimer's disease (AD), an irreversible and progressive condition.

Methods

This study introduces the Multi-Omics Attention Graphical Convolutional Networks (MOAGCN), a novel multilayer deep learning model that addresses the heterogeneity in single-cell multi-omics data to enhance the analysis of multi-omics datasets and uncover potential mechanisms underlying AD. MOAGCN combines Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) to simultaneously capture local cellular connectivity and dynamically weight cell-to-cell interactions. The model was applied to AD-related single-cell RNA-seq and ATAC-seq datasets to identify significant gene expression and epigenetic alterations. It was further validated on datasets including DNA methylation, mRNA, and miRNA from other diseases. The model's performance was compared with conventional methods using metrics such as AUC, accuracy, and F1 scores.

Results

MOAGCN effectively revealed key gene regulatory and protein interaction networks associated with AD, identifying significant changes in gene expression and epigenetic markers. In comparative validation across multiple datasets, MOAGCN outperformed traditional approaches in feature extraction and classification, achieving higher AUC, accuracy, and F1 scores. These results demonstrate its robustness in minimizing false positives and negatives while accurately identifying relevant features.

Discussion

By testing in the classification of cell types and disease samples, MOAGCN achieved remarkable results, showing that its performance outperformed eight leading algorithms in multi-omics data classification tasks. Further analysis of MOAGCN's accuracy revealed a 95% confidence interval for its performance, reinforcing the model's robustness and stability across different datasets.

Conclusion

MOAGCN presents a robust and adaptable framework for integrating single-cell multi-omics data, addressing the challenges of complexity and heterogeneity. Its application to AD datasets highlights its potential to uncover regulatory mechanisms and bio-signals, advancing our understanding of complex diseases. This innovative approach holds promise for broad applications in multi-omics data analysis, particularly in elucidating mechanisms underlying neurodegenerative disorders.

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2025-08-28
2025-12-30

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Attention Graphical Neural Networks-based Single-cell Multi-omics Fusion Analysis of Chromatin Accessibility and Transcriptome Characterization in Alzheimer's Disease
Bentham Science Publishers ; https://doi.org/10.2174/0115748936367139250801163310
/content/journals/cbio/10.2174/0115748936367139250801163310
/content/journals/cbio/10.2174/0115748936367139250801163310
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  • Article Type:     Research Article
Keywords: cell clustering ; deep learning ; Multi-omics data ; chromatin ; sample classification ; Alzheimer's disease

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