ArticleJournal of biomedical semantics2022
A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.
Article in Journal of biomedical semantics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Immune biomarkers, profiles, and responses: a vaccine ontology perspective.Journal of biomedical semantics · 2026Article
- VIRUS-MVP: a framework for comprehensive surveillance of viral mutations and their functional impacts.NAR genomics and bioinformatics · 2025Article
- Three-layered semantic framework for public health intelligence.Journal of biomedical semantics · 2025Article
- A fourfold pathogen reference ontology suite.Journal of biomedical semantics · 2025Article
- A Fourfold Pathogen Reference Ontology Suite.ArXiv · 2025Article
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
- Ontological representation, modeling, and analysis of parasite vaccines.Journal of biomedical semantics · 2024Article
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- Big knowledge visualization of the COVID-19 CIDO ontology evolution.BMC medical informatics and decision making · 2023Article
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- COVoc and COVTriage: novel resources to support literature triage.Bioinformatics (Oxford, England) · 2023Article
- A new framework for host-pathogen interaction research.Frontiers in immunology · 2022Article
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Authors and funding
43 authors at 20 institutions in 5 countries.
Funding
Abstract
backgroundThe current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020.
resultsAs an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment.
conclusionCIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.
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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.