Arabidopsis Interacting Protein Knowledge Graph Dataset

In crop breeding research, protein complexes formed by protein interactions often bind to the promoters of downstream genes to regulate gene transcription and play important biological functions in living organisms. Therefore, the potential discovery of protein complexes is helpful to reveal the structure of protein-protein interaction networks, explore downstream regulatory genes, and better clarify the molecular regulatory mechanisms of traits, which is a key to cultivating new varieties with high quality, high yield and multiple resistances. To break through the limitation of existing knowledge discovery of crop protein complexes that only considers a single influencing factor, the advantage of knowledge graph technology in deep semantic association and knowledge reasoning of multi-dimensional scientific data can be utilized. Using the model plant Arabidopsis thaliana as the data collection object, a dataset of interacting protein knowledge graphs is constructed. The Arabidopsis thaliana protein-protein interaction knowledge graph dataset selects the PlaPPISite database and the Uniprot database as the data acquisition sources, and adopts a mapping knowledge extraction method for structured data to achieve the association and integration of protein-related datasets. Ultimately, an Arabidopsis thaliana protein-protein interaction knowledge graph dataset was formed, which includes 11 entity datasets and 11 entity relationship datasets. This dataset provides a key semantic model and important data foundation for protein function analysis and protein complex discovery.