Motif-Based Hypergraph Representation Learning: Transductive and Inductive Inference for Gene Regulatory Networks

Network motifs, as fundamental functional substructures in gene regulatory networks (GRNs), play a critical role in regulating gene expression. Despite the successful application of graph representation learning in GRN modeling, most existing approaches mainly capture pairwise relationships and overlook higher order regulatory patterns encoded by functional motifs, which limits the accuracy of regulatory inference. To address this limitation, we propose Motif-GRN, a motif-based hypergraph representation learning framework that captures the underlying biological logic in higher order semantic structures. We first identify statistically significant regulatory motifs and construct a multichannel motif-induced hypergraph. We then design a motif-aware hypergraph convolutional network to extract motif-centric semantic features, while a conventional graph convolution module preserves first-order relational information. In addition, we introduce cross-view contrastive learning to align heterogeneous representations and enhance gene embeddings. Building on Motif-GRN, we develop an inductive extension that enables cross-dataset generalization and effective GRN inference with limited labels. Extensive experiments on three ground-truth networks across seven cell types demonstrate that Motif-GRN outperforms state-of-the-art baselines in both transductive and inductive GRN inference tasks, highlighting its potential for higher order regulatory network modeling.