Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution
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 ).
Authors
- Shiyang Liang (ORCID: https://orcid.org/0000-0002-6941-2404)
- Haoyang Li (ORCID: https://orcid.org/0000-0003-3544-5563)
- Jingjie Wang (ORCID: https://orcid.org/0000-0002-2192-7193)
- Yingxu Wang (ORCID: https://orcid.org/0000-0003-3284-1464)
- Jiaqi Xiong
- Nan Yin (ORCID: https://orcid.org/0000-0002-9792-6066)
- Duo Ai
Institutions
- South China Normal University (CN)
- Hong Kong University of Science and Technology (HK)
- Mohamed bin Zayed University of Artificial Intelligence (AE)
- The 180th Hospital of PLA (CN)
- Tang Du Hospital (CN)
- Xijing Hospital (CN)
- University of Hong Kong (HK)
- Air Force Medical University (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1186/s12859-025-06186-1
- Citations
- 1
- Primary Topic
- Gene Regulatory Network Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.81