用图神经网络整合多组学数据，提升跨模态预测准确率
MoRE-GNN: Multi-omics Data Integration with a Heterogeneous Graph Autoencoder
- 基于注意力机制动态构建异质图，融合多组学数据关系
- 在6个公开数据集上表现优于现有方法，尤其在强关联场景
- 结果可解释，适合生物医学研究者做多组学分析
多组学单细胞数据整合面临高维性和复杂模态间关系的挑战。为此，我们提出MoRE-GNN（多组学关系边图神经网络），一种结合图卷积与注意力机制的异质图自编码器，可直接从数据中动态构建关系图。在六个公开数据集上的评估表明，MoRE-GNN能捕捉生物学有意义的关系，且在模态间强相关场景下显著优于现有方法。此外，学习到的表征可实现准确的下游跨模态预测。尽管性能随数据复杂度略有波动，MoRE-GNN提供了一个自适应、可扩展且可解释的多组学整合框架。
原文摘要 · Abstract (English)
The integration of multi-omics single-cell data remains challenging due to high-dimensionality and complex inter-modality relationships. To address this, we introduce MoRE-GNN (Multi-omics Relational Edge Graph Neural Network), a heterogeneous graph autoencoder that combines graph convolution and attention mechanisms to dynamically construct relational graphs directly from data. Evaluations on six publicly available datasets demonstrate that MoRE-GNN captures biologically meaningful relationships and outperforms existing methods, particularly in settings with strong inter-modality correlations. Furthermore, the learned representations allow for accurate downstream cross-modal predictions. While performance may vary with dataset complexity, MoRE-GNN offers an adaptive, scalable and interpretable framework for advancing multi-omics integration.
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