RNA-KG v2.0: An RNA-centered Knowledge Graph with Properties

Abstract

RNA-KG is a recently developed 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.

References in corpus (5)

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• LINE: Large-scale Information Network Embedding
• Knowledge Graphs
• RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules
• BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs