ArticleBioinformatics (Oxford, England)2023
BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- MetagenomicKG: a knowledge graph for metagenomic applications.Bioinformatics (Oxford, England) · 2026Article
- Federated knowledge retrieval elevates large language model performance on biomedical benchmarks.GigaScience · 2026Article
- 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
- Playbook workflow builder: Interactive construction of bioinformatics workflows.PLoS computational biology · 2025Article
- Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data.bioRxiv : the preprint server for biology · 2024Article
- MetagenomicKG: a knowledge graph for metagenomic applications.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
- Update of
Authors and funding
14 authors.
Funding
Abstract
summaryKnowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time. AVAILABILITY AND IMPLEMENTATION: More information can be found at https://explorer.biothings.io and code is available at https://github.com/biothings/biothings_explorer.
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.