Abstract RNA-KG is a recently developed biomedical knowledge graph that integrates the interactions involving coding and non-coding RNA molecules extracted from public data sources. It can be used to support the classification of new molecules, identify new interactions through the use of link prediction methods, and reveal hidden patterns among the represented entities. In this paper, we propose RNA-KG v2. 0, a new release of RNA-KG that integrates around 100M manually curated interactions sourced from 91 linked open data repositories and ontologies. Relationships are characterized by standardized properties that capture the specific context (e. g. cell line, tissue, pathological state) in which they have been identified. In addition, the nodes are enriched with detailed attributes, such as descriptions, synonyms, and molecular sequences sourced from platforms such as OBO ontologies, NCBI repositories, RNAcentral, and Ensembl. The enhanced repository enables the expression of advanced queries that take into account the context in which the experiments were conducted. It also supports downstream applications in RNA research, including ‘context-aware’ link prediction techniques that combine both topological and semantic information. Finally, the recent integration of RNA-KG relationships into the RNAcentral portal provides a powerful resource for linking RNA-centric relationships with non-coding gene expression in human tissues, RNA secondary structures, and their functional roles in biological pathways, which can accelerate the discovery of novel therapeutic targets.
Cavalleri et al. (Sat,) studied this question.