Evidence map›Paper›PMID 26395080›Full record

ArticleTheoretical biology & medical modelling2015

PDON: Parkinson's disease ontology for representation and modeling of the Parkinson's disease knowledge domain.

Erfan Younesi, Ashutosh Malhotra, Michaela Gündel, Phil Scordis, Alpha Tom Kodamullil, Matt Page, Bernd Müller, Stephan Springstubbe, Ullrich Wüllner, Dieter Scheller and 1 more

Abstract read
In one paragraph

Article in Theoretical biology & medical modelling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Biomedical Holistic Ontology for People with Rare Diseases.International journal of environmental research and public health · 2020
    Article
  9. Review
  10. Article
  11. Article
  12. Review
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

11 authors.

Erfan YounesiDepartment of Bionformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. erfan.younesi@scai.fraunhofer.de.
Ashutosh MalhotraDepartment of Bionformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. ashutosh.malhotra@scai.fraunhofer.de.
Michaela GündelDepartment of Bionformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. michaela.guendel@scai-extern.fraunhofer.de.
Phil ScordisInformatics group, UCB Pharma, 208 Bath Road, Slough, UK. phil.scordis@ucb.com.
Alpha Tom KodamullilDepartment of Bionformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. alpha.tom.kodamullil@scai.fraunhofer.de.
Matt PageInformatics group, UCB Pharma, 208 Bath Road, Slough, UK. matt.page@ucb.com.
Bernd MüllerDepartment of Bionformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. bernd.mueller@scai.fraunhofer.de.
Stephan SpringstubbeDepartment of Bionformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. stephan.springstubbe@scai.fraunhofer.de.
Ullrich WüllnerDepartment of Neurology, University of Bonn, 53105, Bonn, Germany. ullrich.wuellner@ukb.uni-bonn.de.
Dieter SchellerPharmacology Parkinson's Disease and Movement Disorders, UCB Pharma S.A., Chemin du Foriest, B-1420, Braine-l'Allued, Belgium. dieter.scheller@ucb.com.
Martin Hofmann-ApitiusDepartment of Bionformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. martin.hofmann-apitius@scai.fraunhofer.de.

Funding

NIGMS NIH HHS GM10331601
6 · The paper itself

Abstract

backgroundDespite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism.

methodsThe Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology.

resultsThe Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson's disease.

conclusionsParkinson's disease ontology delivers the knowledge domain of Parkinson's disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson's-related computable models. A practical version of the Parkinson's disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON .

Indexed as

Gene OntologyKnowledgeSoftwareAnimalsDatabases, GeneticDisease Models, AnimalGene Expression RegulationGene Regulatory NetworksHumansMolecular Sequence AnnotationParkinson Disease

Identifiers

PMID26395080
PMCPMC4580356

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.