School of Automation and Information Engineering, Xi 'an University of Technology, No.5, Jinhua South Road, Xi'an, 710048, Shaanxi, China.
Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing, Xi'an, 710048, Shaanxi, China.
Sci Rep. 2024 Sep 12;14(1):21342. doi: 10.1038/s41598-024-71864-8.
Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel Autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable.
通过深度学习和因果推理方法推断基因调控网络是计算生物学和生物信息学领域的一项关键任务。本研究提出了一种新方法，该方法使用因果信息引导的图卷积网络（GCN）来推断基因调控网络（GRN）。利用转移熵和重构层实现因果特征重构，减轻了 GCN 中多轮邻居聚合导致的信息丢失问题，从而实现了节点特征的因果和综合表示。通过高斯核自动编码器从基因表达数据中提取可分离特征，以提高计算效率。在 DREAM5 和 mDC 数据集上的实验结果表明，与现有算法相比，我们的方法表现出更好的性能，AUPRC 指标的值更高。此外，因果特征重构的引入增强了推断的 GRN，使其更加合理、准确和可靠。
