ArticleBioinformatics (Oxford, England)2021
COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph databases in systems biology: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.Briefings in bioinformatics · 2026Review
- A Narrative Review of Artificial Intelligence for Drug Repurposing: Lessons From COVID-19 and Oncology (2020-2025).CPT: pharmacometrics & systems pharmacology · 2026Review
- Leveraging knowledge graphs and large language models for integrating molecular variants and clinical insights in COVID-19 research.Biosafety and health · 2026Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Review
- KGG: a fully automated workflow for creating disease-specific knowledge graphs.Bioinformatics (Oxford, England) · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data.Scientific data · 2024Article
- Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.Frontiers in molecular biosciences · 2024Article
- A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning.Frontiers in big data · 2024Article
- BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs.Journal of biomedical semantics · 2023Article
- Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations.Database : the journal of biological databases and curation · 2023Article
- The importance of graph databases and graph learning for clinical applications.Database : the journal of biological databases and curation · 2023Review
- KG-Hub-building and exchanging biological knowledge graphs.Bioinformatics (Oxford, England) · 2023Article
- Rewiring Drug Research and Development through Human Data-Driven Discovery (HDPharmaceutics · 2023Review
- A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network.Entropy (Basel, Switzerland) · 2023Article
- Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature.Journal of biomedical informatics · 2023Article
- Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora.Computational biology and chemistry · 2023Article
- Knowledge Graph: Applications in Tracing the Source of Large-Scale Outbreak - Beijing Municipality, China, 2020-2021.China CDC weekly · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summaryThe COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.