Wenzhong Huang

Biomass Research Center, Hainan Institute of Tropical Agricultural Resouces, Sanya, 572025, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 4   doi: 10.5376/cmb.2025.15.0017
Received: 12 May, 2025    Accepted: 23 Jun., 2025    Published: 15 Jul., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Huang W.Z., 2025, Knowledge graph construction for molecular interaction exploration, Computational Molecular Biology, 15(4): 171-182 (doi: 10.5376/cmb.2025.15.0017)

 

In recent years, knowledge graph technology has emerged in bioinformatics, providing new ideas for the study of interaction relationships at the molecular level. This research focuses on the construction and analysis of the "Molecular Interaction Knowledge Graph", including the integration and preprocessing of data sources, the construction methods of the knowledge graph, the representation and analysis techniques of the graph, as well as the case study and system implementation of the protein-protein interaction knowledge graph. The research first sorted out the current application status of knowledge graphs in bioinformatics, and clarified the background significance and innovation points of constructing molecular interaction knowledge graphs. Subsequently, the standardization and entity semantic normalization strategies for multi-source biological data were discussed, and the modeling methods for entities and relationships as well as the automated construction process were proposed. In terms of graph analysis, key technologies such as knowledge representation learning, network topology analysis, semantic reasoning and relationship prediction are reviewed. Through the case of protein-protein interaction mapping, the specific process of mapping construction, visualization results and biological verification are presented, and the biological significance of the conclusions obtained is discussed. Finally, the current challenges in the field of molecular interaction knowledge graphs, such as data heterogeneity, model interpretability and knowledge uncertainty, are summarized, and the future development directions are prospected. The research work is expected to provide a solid knowledge support for promoting the systematic analysis of complex molecular networks and biomedical discoveries.

Molecular interaction; Knowledge graph; Bioinformatics; Protein-protein interactions; Knowledge representation learning