ArticleBioinformatics (Oxford, England)2019
ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- The ROBOKOP v1.0 knowledge graph system for exploring relationships between biomedical entities.Scientific reports · 2026Article
- Medicines, Diseases, Indications, and Contraindications (MeDIC): a foundational resource to support drug repurposing.Nucleic acids research · 2026Article
- Article
- PloverDB: a high-performance platform for serving biomedical knowledge graphs as standards-compliant web APIs.Bioinformatics (Oxford, England) · 2025Article
- Breaking the Phalanx: Overcoming Bacterial Drug Resistance with Quorum Sensing Inhibitors that Enhance Therapeutic Activity of Antibiotics.bioRxiv : the preprint server for biology · 2025Article
- PloverDB: A high-performance platform for serving biomedical knowledge graphs as standards-compliant web APIs.bioRxiv : the preprint server for biology · 2025Article
- The Six Ds of Exponentials and drug discovery: A path toward reversing Eroom's law.Drug discovery today · 2025Review
- Elucidating the mechanistic relationships between peroxisome proliferator-activated receptors and hepatic fibrosis using the ROBOKOP knowledge graph.Frontiers in toxicology · 2025Article
- Advancement of Computational Design Drug Delivery System in COVID-19: Current Updates and Future Crosstalk- A Critical update.Infectious disorders drug targets · 2023Article
- ARAX: a graph-based modular reasoning tool for translational biomedicine.Bioinformatics (Oxford, England) · 2023Article
- Lead/Drug Discovery from Natural Resources.Molecules (Basel, Switzerland) · 2022Review
- RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine.BMC bioinformatics · 2022Article
- Automating Predictive Toxicology Using ComptoxAI.Chemical research in toxicology · 2022Article
- Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination.BMC bioinformatics · 2022Article
- Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.Frontiers in pharmacology · 2022Article
- Application of MCAT questions as a testing tool and evaluation metric for knowledge graph-based reasoning systems.Clinical and translational science · 2021Article
- A critical overview of computational approaches employed for COVID-19 drug discovery.Chemical Society reviews · 2021Review
- A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-World Environmental Health Observations: Cohort Study and Informatics Application.JMIR medical informatics · 2021Article
- Synergistic and Antagonistic Drug Combinations against SARS-CoV-2.Molecular therapy : the journal of the American Society of Gene Therapy · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
summaryKnowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results. AVAILABILITY AND IMPLEMENTATION: An instance of the ROBOKOP UI for exploration of the ROBOKOP Knowledge Graph can be found at http://robokop.renci.org. The ROBOKOP Knowledge Graph can be accessed at http://robokopkg.renci.org. Code and instructions for building and deploying ROBOKOP are available under the MIT open software license from https://github.com/NCATS-Gamma/robokop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.