Reconstructing gene regulatory networks (GRNs) is a fundamental challenge in bioinformatics that aims to unravel the complex relationships between genes and their regulators. Graph convolutional neural networks have shown more significant improvements in this field than traditional methods. However, GCNs rely heavily on smooth node features rather than graph structures. To address this limitation, Two-layer Neighbor Overlapping Perceptual Graph Convolution Network (Tnop-GCN) is proposed, that jointly learns local and global structural features by PageRank and DeepWalk. Experiments on DREAM4 dataset demonstrate that Tnop-GCN outperforms many other gene regulatory network reconstruction methods.
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