ArticleBMC bioinformatics2011
Logical development of the cell ontology.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
100 citing papers in PubMed, 1 synthesis or guideline pooled it, 147 citations in OpenAlex.
- Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database.Cancer research · 2023Pooled it
- DNAviWEB: sequencing-free clinical screening of liquid biopsies.Nucleic acids research · 2026Article
- The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors.Cell genomics · 2026Article
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching.Nature communications · 2026Article
- Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas.Scientific data · 2026Article
- Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations.bioRxiv : the preprint server for biology · 2025Article
- Constructing and Using Cell Type Populations of the Human Reference Atlas.bioRxiv : the preprint server for biology · 2025Article
- A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography.bioRxiv : the preprint server for biology · 2025Article
- High tissue specificity of lncRNAs maximises the prediction of tissue of origin of circulating DNA.Scientific reports · 2025Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage.Nature methods · 2025Article
- Annotation of biological samples data to standard ontologies with support from large language models.Computational and structural biotechnology journal · 2025Article
- Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data.Scientific data · 2024Article
- Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation.Nucleic acids research · 2024Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.bioRxiv : the preprint server for biology · 2024Article
- An ontological framework for organising and describing behaviours: The Human Behaviour Ontology.Wellcome open research · 2024Article
- Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP).Nature cell biology · 2023Review
- WebCSEA: web-based cell-type-specific enrichment analysis of genes.Nucleic acids research · 2022Article
- Self-supervised learning of cell type specificity from immunohistochemical images.Bioinformatics (Oxford, England) · 2022Article
- A census of the lung: CellCards from LungMAP.Developmental cell · 2022Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundThe Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.
resultsComputable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.
conclusionUse of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.
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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.