BioKG Knowledge Graph

References

(Gema et al., 2023) ⇒ Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf, Piyush Borole, Javier Antonio Alfaro, Pasquale Minervini, Antonio Vergari, and Ajitha Rajan. (2023). “Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks.” doi:10.48550/arXiv.2305.19979
- QUOTE: Fig. 1. An extract of BioKG [23]. The nodes represent entities in the KG, edges between them are links. The variety in identifier structure shows that BioKG is a combination of multiple smaller KGs. In this extract, the centre node (DB00860) represents the drug prednisolone, which targets the Glucocorticoid receptor (P04150). This receptor is associated with disorders related to or resulting from the use of cocaine (D019970), indicated by the Protein-Disease-Association relation (PDiA). Hence, prednisolone is connected to said disorders through the Drug-Disease-Association relation (DrDiA). The right-most node (D000544) represents Alzheimer’s disease, a genetic disorder (GeDi). One possible application that uses the information in the KG would be to train a model to predict missing links. Such a model could consider information from, for example, the Drug-Drug Interaction (DDI) and Protein-Protein Interaction (PPI) relations starting from prednisolone to predict that prednisolone could also be used to treat Alzheimer’s disease, as indicated by the dashed DrDiA relation between them.