Evidence map›Paper›PMID 18460183›Full record

ArticleBMC bioinformatics2008

Ontology Design Patterns for bio-ontologies: a case study on the Cell Cycle Ontology.

Mikel Egaña Aranguren, Erick Antezana, Martin Kuiper, Robert Stevens

Abstract read
In one paragraph

Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.Database : the journal of biological databases and curation · 2019
    Article
  3. Article
  4. Review
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  15. Applications of ontology design patterns in biomedical ontologies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012
    Article
  16. Predicting the extension of biomedical ontologies.PLoS computational biology · 2012
    Article
  17. Article
  18. Semi-automated ontology generation within OBO-Edit.Bioinformatics (Oxford, England) · 2010
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mikel Egaña ArangurenSchool of Computer Science, University of Manchester, Oxford Road, M13 9PL Manchester, UK. mikel.eganaaranguren@cs.man.ac.uk
Erick Antezana
Martin Kuiper
Robert Stevens

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBio-ontologies are key elements of knowledge management in bioinformatics. Rich and rigorous bio-ontologies should represent biological knowledge with high fidelity and robustness. The richness in bio-ontologies is a prior condition for diverse and efficient reasoning, and hence querying and hypothesis validation. Rigour allows a more consistent maintenance. Modelling such bio-ontologies is, however, a difficult task for bio-ontologists, because the necessary richness and rigour is difficult to achieve without extensive training.

resultsAnalogous to design patterns in software engineering, Ontology Design Patterns are solutions to typical modelling problems that bio-ontologists can use when building bio-ontologies. They offer a means of creating rich and rigorous bio-ontologies with reduced effort. The concept of Ontology Design Patterns is described and documentation and application methodologies for Ontology Design Patterns are presented. Some real-world use cases of Ontology Design Patterns are provided and tested in the Cell Cycle Ontology. Ontology Design Patterns, including those tested in the Cell Cycle Ontology, can be explored in the Ontology Design Patterns public catalogue that has been created based on the documentation system presented (http://odps.sourceforge.net/).

conclusionsOntology Design Patterns provide a method for rich and rigorous modelling in bio-ontologies. They also offer advantages at different development levels (such as design, implementation and communication) enabling, if used, a more modular, well-founded and richer representation of the biological knowledge. This representation will produce a more efficient knowledge management in the long term.

Indexed as

Cell CycleUser-Computer InterfaceArtificial IntelligenceComputational BiologyDatabase Management SystemsDatabases, FactualModels, BiologicalPattern Recognition, AutomatedSemanticsVocabulary, Controlled

Identifiers

PMID18460183
PMCPMC2367624

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.