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Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution
Xiong, Jiaqi Yin, Nan Liang, Shiyang Li, Haoyang Wang, Yingxu Ai, Duo Wang, Jingjie
Xiong, Jiaqi
Yin, Nan
Liang, Shiyang
Li, Haoyang
Wang, Yingxu
Ai, Duo
Wang, Jingjie
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Background: Inferring Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology. Most existing methods fail to consider the skewed degree distribution of genes, complicating the application of directed graph embedding methods. Results: The Cross-Attention Complex Dual Graph Embedding Model (XATGRN) was proposed to address this issue. It employs a cross-attention mechanism and a dual complex graph embedding approach to manage the skewed degree distribution, ensuring precise prediction of regulatory relationships and their directionality. The model consistently outperforms existing state-of-the-art methods across various datasets. Conclusions: XATGRN provides an effective solution for inferring GRNs with skewed degree distribution, enhancing the understanding of complex gene regulatory mechanisms. The codes and detailed requirements have been released on Github: (https://github.com/kikixiong/XATGRN).
Citation
J. Xiong et al., “Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution,” BMC Bioinformatics, vol. 26, no. 1, pp. 1–21, Dec. 2025, doi: 10.1186/S12859-025-06186-1/FIGURES/7
Source
BMC Bioinformatics
Conference
Keywords
Cross attention network, Direct graph embedding, Gene regulatory network
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Source
Publisher
Springer Nature