针对基因调控网络的度分布偏斜问题，提出跨注意力图神经网络模型。
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
- 引入交叉注意力机制捕捉基因间复杂互作关系。
- 采用双复数图嵌入处理基因调控度分布偏斜问题。
- 在多个数据集上优于现有方法，适合生物网络研究者使用。
从基因表达数据推断基因调控网络（GRNs）是系统生物学中的关键挑战，已有多种计算方法被提出。然而，多数研究未考虑基因的度分布偏斜问题：部分基因调控多个靶基因，而部分基因受多个调节基因影响。这种度分布偏斜显著增加了有向图嵌入方法的应用难度。为此，我们提出交叉注意力复杂双图嵌入模型（XATGRN）。该模型利用交叉注意力机制从基因表达谱中有效捕捉复杂的基因互作关系，并采用双复数图嵌入方法处理度分布偏斜，从而精确预测调控关系及其方向性。在多个数据集上，本模型持续优于现有最先进方法，验证了其在解析复杂基因调控机制方面的有效性。本文代码已公开于：https://github.com/kikixiong/XATGRN。
原文摘要 · Abstract (English)
Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not considered the skewed degree distribution of genes. Specifically, some genes may regulate multiple target genes while some genes may be regulated by multiple regulator genes. Such a skewed degree distribution issue significantly complicates the application of directed graph embedding methods. To tackle this issue, we propose the Cross-Attention Complex Dual Graph Embedding Model (XATGRN). Our XATGRN employs a cross-attention mechanism to effectively capture intricate gene interactions from gene expression profiles. Additionally, it uses a Dual Complex Graph Embedding approach to manage the skewed degree distribution, thereby ensuring precise prediction of regulatory relationships and their directionality. Our model consistently outperforms existing state-of-the-art methods across various datasets, underscoring its efficacy in elucidating complex gene regulatory mechanisms. Our codes used in this paper are publicly available at: https://github.com/kikixiong/XATGRN.
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