<ul><li>Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology.</li><li>Most studies have not considered the skewed degree distribution of genes, which complicates the application of directed graph embedding methods.</li><li>To address this issue, the Cross-Attention Complex Dual Graph Embedding Model (XATGRN) is proposed.</li><li>XATGRN effectively captures intricate gene interactions and accurately predicts regulatory relationships and their directionality.</li></ul>

Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution

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