Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution
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-Atten...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06186-1 |
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| author | Jiaqi Xiong Nan Yin Shiyang Liang Haoyang Li Yingxu Wang Duo Ai Jingjie Wang |
| author_facet | Jiaqi Xiong Nan Yin Shiyang Liang Haoyang Li Yingxu Wang Duo Ai Jingjie Wang |
| author_sort | Jiaqi Xiong |
| collection | DOAJ |
| description | 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 ). |
| format | Article |
| id | doaj-art-d58205548a6743d9a39246f2e841fb46 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-d58205548a6743d9a39246f2e841fb462025-08-20T04:02:42ZengBMCBMC Bioinformatics1471-21052025-07-0126112110.1186/s12859-025-06186-1Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distributionJiaqi Xiong0Nan Yin1Shiyang Liang2Haoyang Li3Yingxu Wang4Duo Ai5Jingjie Wang6Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal UniversityDepartment of Computer Science and Engineering, Hong Kong University of Science and TechnologyDepartment of Internal Medicine, The No. 944 Hospital of Joint Logistic Support Force of PLAAberdeen Institute of Data Science and Artificial Intelligence, South China Normal UniversityDepartment of Machine Learning, Mohamed bin Zayed University of Artificial IntelligenceDepartment of Dermatology, Xijing Hospital, Fourth Military Medical UniversityDepartment of Gastroenterology, Tangdu Hospital, Fourth Military Medical UniversityAbstract 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 ).https://doi.org/10.1186/s12859-025-06186-1Gene regulatory networkDirect graph embeddingCross attention network |
| spellingShingle | Jiaqi Xiong Nan Yin Shiyang Liang Haoyang Li Yingxu Wang Duo Ai Jingjie Wang Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution BMC Bioinformatics Gene regulatory network Direct graph embedding Cross attention network |
| title | Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution |
| title_full | Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution |
| title_fullStr | Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution |
| title_full_unstemmed | Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution |
| title_short | Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution |
| title_sort | cross attention graph neural networks for inferring gene regulatory networks with skewed degree distribution |
| topic | Gene regulatory network Direct graph embedding Cross attention network |
| url | https://doi.org/10.1186/s12859-025-06186-1 |
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