Evidence map›Paper›PMID 28592275›Full record

ArticleJournal of biomedical semantics2017

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

Marcos Martínez-Romero, Clement Jonquet, Martin J O'Connor, John Graybeal, Alejandro Pazos, Mark A Musen

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  10. Knowledge Graph Embeddings for ICU readmission prediction.BMC medical informatics and decision making · 2023
    Article
  11. Article
  12. [Ontologies Applied in Clinical Decision Support Systems for Diabetes].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2023
    Review
  13. Article
  14. Article
  15. Article
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  19. Article
  20. A Second Look at FAIR in Proteomic Investigations.Journal of proteome research · 2021
    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

6 authors.

Marcos Martínez-RomeroStanford Center for Biomedical Informatics Research, 1265 Welch Road, Stanford University School of Medicine, Stanford, CA, 94305-5479, USA. marcosmr@stanford.edu.ORCID http://orcid.org/0000-0002-9814-3258
Clement JonquetStanford Center for Biomedical Informatics Research, 1265 Welch Road, Stanford University School of Medicine, Stanford, CA, 94305-5479, USA.
Martin J O'ConnorStanford Center for Biomedical Informatics Research, 1265 Welch Road, Stanford University School of Medicine, Stanford, CA, 94305-5479, USA.
John GraybealStanford Center for Biomedical Informatics Research, 1265 Welch Road, Stanford University School of Medicine, Stanford, CA, 94305-5479, USA.
Alejandro PazosDepartment of Information and Communication Technologies, Computer Science Building, Elviña Campus, University of A Coruña, 15071, A Coruña, Spain.
Mark A MusenStanford Center for Biomedical Informatics Research, 1265 Welch Road, Stanford University School of Medicine, Stanford, CA, 94305-5479, USA.

Funding

STANFORD COMPOSITE(RMI)U54HG004028 · NHGRI · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2005 to 2014
$36.2M
TrainingU54AI117925 · NIAID · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2014 to 2018
$12.9M
NHGRI NIH HHS U54 HG004028NIAID NIH HHS U54 AI117925
6 · The paper itself

Abstract

backgroundOntologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms.

methodsWe developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data.

resultsOur evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios.

conclusionsOntology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender , and via a Web service API).

Indexed as

Biological OntologiesNational Institutes of Health (U.S.)SemanticsUnited StatesBiomedical ontologiesNCBO BioPortalOntology evaluationOntology recommendationOntology selectionSemantic Web

Identifiers

PMID28592275
PMCPMC5463318

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.