Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution

Jiaqi Xiong, Nan Yin, Shiyang Liang, Haoyang Li, Yingxu Wang, Duo Ai, Jingjie Wang

Abstract: 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. 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. 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 ).