Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs

AbstractSince gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF) –target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural information of GRNs in gene expression profiles. Therefo re, we propose a multi-view hierarchical hypergraphs GRN (MHHGRN) inference model. Specifically, multiple heterogeneous biological information is integrated to construct multi-view hierarchical hypergraphs of TFs and target genes, using hypergraph convolution networks to model higher order complex r egulatory relationships. Meanwhile, the coupled information diffusion mechanism and the cross-domain messaging mechanism facilitate the information sharing between genes to optimise gene embedding representations. Finally, a unique channel attention mechanism is used to adaptively learn feature repr esentations from multiple views for GRN inference. Experimental results show that MHHGRN achieves better results than the baseline methods on theE. coli andS. cerevisiae benchmark datasets of the DREAM5 challenge, and...
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research