Evidence map›Paper›PMID 20971987›Full record

ArticleBioinformatics (Oxford, England)2010

Interoperability between phenotype and anatomy ontologies.

Robert Hoehndorf, Anika Oellrich, Dietrich Rebholz-Schuhmann

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Integrating phenotype ontologies with PhenomeNET.Journal of biomedical semantics · 2017
    Article
  6. Article
  7. Article
  8. The digital revolution in phenotyping.Briefings in bioinformatics · 2016
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Evaluation of research in biomedical ontologies.Briefings in bioinformatics · 2013
    Article
  16. Using ontologies to study cell transitions.Journal of biomedical semantics · 2013
    Article
  17. Article
  18. Article
  19. Computational tools for comparative phenomics: the role and promise of ontologies.Mammalian genome : official journal of the International Mammalian Genome Society · 2012
    Review
  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

3 authors.

Robert HoehndorfEuropean Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. rh497@cam.ac.uk
Anika Oellrich
Dietrich Rebholz-Schuhmann

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationPhenotypic information is important for the analysis of the molecular mechanisms underlying disease. A formal ontological representation of phenotypic information can help to identify, interpret and infer phenotypic traits based on experimental findings. The methods that are currently used to represent data and information about phenotypes fail to make the semantics of the phenotypic trait explicit and do not interoperate with ontologies of anatomy and other domains. Therefore, valuable resources for the analysis of phenotype studies remain unconnected and inaccessible to automated analysis and reasoning.

resultsWe provide a framework to formalize phenotypic descriptions and make their semantics explicit. Based on this formalization, we provide the means to integrate phenotypic descriptions with ontologies of other domains, in particular anatomy and physiology. We demonstrate how our framework leads to the capability to represent disease phenotypes, perform powerful queries that were not possible before and infer additional knowledge. AVAILABILITY: http://bioonto.de/pmwiki.php/Main/PheneOntology.

Indexed as

PhenotypeVocabulary, ControlledDiseaseHumansSemantics

Identifiers

PMID20971987
PMCPMC2995119

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Registered trials

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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.