Accurate identification of protein complexes has significant scientific and practical value in disease research, bioengineering, and understanding life activities. Current identification algorithms are based on graph structures and do not consider the high-order nature of interactions between proteins, resulting in insufficient identification quality and efficiency. Considering the advantages of hypergraphs in handling high-order data and computational efficiency, a protein complex identification algorithm based on a dynamic hypergraph neural network (PCI-DHNN) is proposed. First, considering the dynamic and time-varying characteristics of protein networks, the protein network is sliced into time slices using the activity cycle of proteins. Then, dynamic protein subnetworks are constructed based on hypergraph theory. On this basis, a hypergraph convolutional operator is designed for the feature learning of protein subnetworks to obtain a membership matrix describing the potential relationships between proteins and complexes. Finally, the protein complexes were identified using a Bernoulli mixture model. Experimental simulation results on four protein network datasets of yeast species show that compared with current typical protein complex identification algorithms, the PCI-DHNN performs better in terms of recall, precision, F1-score, coverage rate, and functional enrichment, and has higher reliability.
Li et al. (2026) studied this question.