ArticleBMC bioinformatics2022
RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph databases in systems biology: a systematic review.Briefings in bioinformatics · 2024Pooled it
- MetagenomicKG: a knowledge graph for metagenomic applications.Bioinformatics (Oxford, England) · 2026Article
- The ROBOKOP v1.0 knowledge graph system for exploring relationships between biomedical entities.Scientific reports · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Large Language Models in Bio-Ontology Research: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- PloverDB: a high-performance platform for serving biomedical knowledge graphs as standards-compliant web APIs.Bioinformatics (Oxford, England) · 2025Article
- PloverDB: A high-performance platform for serving biomedical knowledge graphs as standards-compliant web APIs.bioRxiv : the preprint server for biology · 2025Article
- Knowledge graphs in psychiatric research: Potential applications and future perspectives.Acta psychiatrica Scandinavica · 2025Review
- Pioneering a new field of computational pharmacophenomics to unlock the life-saving potential of existing medicines.The Lancet. Haematology · 2025Article
- An open source knowledge graph ecosystem for the life sciences.Scientific data · 2024Article
- MetagenomicKG: a knowledge graph for metagenomic applications.bioRxiv : the preprint server for biology · 2024Article
- Semantics-enabled biomedical literature analytics.Journal of biomedical informatics · 2024Article
- Article
- BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.Bioinformatics (Oxford, England) · 2023Article
- Knowledge Representation and Management 2022: Findings in Ontology Development and Applications.Yearbook of medical informatics · 2023Review
- KG-Hub-building and exchanging biological knowledge graphs.Bioinformatics (Oxford, England) · 2023Article
- Article
- ARAX: a graph-based modular reasoning tool for translational biomedicine.Bioinformatics (Oxford, England) · 2023Article
- Hetnet connectivity search provides rapid insights into how two biomedical entities are related.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
backgroundBiomedical 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).
resultsTo 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.
conclusionRTX-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 .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.