FORUM: building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases

Motivation: Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. Results: The use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. Supplementary information: Supplementary data are available at Bioinformatics online.

Automated summary

This study introduces the application of a knowledge graph in metabolomics research using a semantic web framework. Through reasoning new relations between entities, different levels of abstraction are revealed, potentially leading to new hypotheses. The statistical relevance of extracted relations is estimated through enrichment analysis, instantiated as new knowledge in the knowledge graph to support results interpretation and further inquiries.