RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

Abstract

Background Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). Results To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building—and hosting a web API for querying—a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. Conclusion RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2.

A multimodal intervention for Alzheimer’s disease results in multifaceted systemic effects reflected in blood and ameliorates functional and cognitive outcomes

“…For example, hypotaurine is a metabolite near the bottom of our candidate list; at least two articles in PubMed include both ‘hypotaurine’ and ‘Alzheimer’ in their Abstracts and could be plausibly linked to AD using arguments similar to the arguments for consistency that we present above, although perhaps not as strongly. There are methods of quantifying the surprise, or significance of connectivity of set of bioentities to AD, typically operating on knowledge graphs such as underpin Translator (Wood et al, 2022). These methods require some simplifications, as they are currently not able to capture all nuances of relationship; we defer such objective analysis to a subsequent manuscript.…”

Hetnet connectivity search provides rapid insights into how two biomedical entities are related

“…Other integrative resources, some still under development, include Wikidata [34], SemMedDB [35,36,37]. SPOKE, and RTX-KG2c [38].…”

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

“…We obtained 387 papers after the section editors' initial screening. The section editors further reviewed these papers jointly and reached a consensus list of 15 papers, which were nominated as the candidate best papers [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. External reviewers, IMIA Yearbook editors and section editors further evaluated these 15 papers and finally selected two best papers (see Table 1).…”