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The Bone Dysplasia Ontology: integrating genotype and phenotype information in the skeletal dysplasia domain

Overview of attention for article published in BMC Bioinformatics, March 2012
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

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32 X users

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Title
The Bone Dysplasia Ontology: integrating genotype and phenotype information in the skeletal dysplasia domain
Published in
BMC Bioinformatics, March 2012
DOI 10.1186/1471-2105-13-50
Pubmed ID
Authors

Tudor Groza, Jane Hunter, Andreas Zankl

Abstract

Skeletal dysplasias are a rare and heterogeneous group of genetic disorders affecting skeletal development. Patients with skeletal dysplasias suffer from many complex medical issues including degenerative joint disease and neurological complications. Because the data and expertise associated with this field is both sparse and disparate, significant benefits will potentially accrue from the availability of an ontology that provides a shared conceptualisation of the domain knowledge and enables data integration, cross-referencing and advanced reasoning across the relevant but distributed data sources. We introduce the design considerations and implementation details of the Bone Dysplasia Ontology. We also describe the different components of the ontology, including a comprehensive and formal representation of the skeletal dysplasia domain as well as the related genotypes and phenotypes. We then briefly describe SKELETOME, a community-driven knowledge curation platform that is underpinned by the Bone Dysplasia Ontology. SKELETOME enables domain experts to use, refine and extend and apply the ontology without any prior ontology engineering experience--to advance the body of knowledge in the skeletal dysplasia field. The Bone Dysplasia Ontology represents the most comprehensive structured knowledge source for the skeletal dysplasias domain. It provides the means for integrating and annotating clinical and research data, not only at the generic domain knowledge level, but also at the level of individual patient case studies. It enables links between individual cases and publicly available genotype and phenotype resources based on a community-driven curation process that ensures a shared conceptualisation of the domain knowledge and its continuous incremental evolution.

X Demographics

X Demographics

The data shown below were collected from the profiles of 32 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 8%
Brazil 2 5%
Netherlands 1 3%
Unknown 33 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 23%
Student > Ph. D. Student 6 15%
Student > Master 5 13%
Professor > Associate Professor 3 8%
Student > Bachelor 3 8%
Other 6 15%
Unknown 7 18%
Readers by discipline Count As %
Computer Science 12 31%
Medicine and Dentistry 9 23%
Agricultural and Biological Sciences 4 10%
Biochemistry, Genetics and Molecular Biology 3 8%
Engineering 2 5%
Other 2 5%
Unknown 7 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 November 2021.
All research outputs
#1,440,869
of 25,591,967 outputs
Outputs from BMC Bioinformatics
#182
of 7,722 outputs
Outputs of similar age
#7,518
of 172,865 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 77 outputs
Altmetric has tracked 25,591,967 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,722 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 172,865 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.