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Framework for a Protein Ontology

Overview of attention for article published in BMC Bioinformatics, November 2007
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Title
Framework for a Protein Ontology
Published in
BMC Bioinformatics, November 2007
DOI 10.1186/1471-2105-8-s9-s1
Pubmed ID
Authors

Darren A Natale, Cecilia N Arighi, Winona C Barker, Judith Blake, Ti-Cheng Chang, Zhangzhi Hu, Hongfang Liu, Barry Smith, Cathy H Wu

Abstract

Biomedical ontologies are emerging as critical tools in genomic and proteomic research, where complex data in disparate resources need to be integrated. A number of ontologies describe properties that can be attributed to proteins. For example, protein functions are described by the Gene Ontology (GO) and human diseases by SNOMED CT or ICD10. There is, however, a gap in the current set of ontologies - one that describes the protein entities themselves and their relationships. We have designed the PRotein Ontology (PRO) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modifications). PRO will allow the specification of relationships between PRO, GO and other ontologies in the OBO Foundry. Here we describe the initial development of PRO, illustrated using human and mouse proteins involved in the transforming growth factor-beta and bone morphogenetic protein signaling pathways.

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
United States 6 8%
United Kingdom 5 7%
Germany 2 3%
Brazil 1 1%
India 1 1%
Spain 1 1%
Canada 1 1%
Unknown 57 77%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 35%
Student > Ph. D. Student 12 16%
Student > Master 11 15%
Professor > Associate Professor 8 11%
Student > Bachelor 4 5%
Other 10 14%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 41%
Computer Science 14 19%
Biochemistry, Genetics and Molecular Biology 11 15%
Medicine and Dentistry 4 5%
Philosophy 2 3%
Other 10 14%
Unknown 3 4%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 August 2013.
All research outputs
#13,358,992
of 22,660,862 outputs
Outputs from BMC Bioinformatics
#4,187
of 7,241 outputs
Outputs of similar age
#127,627
of 155,829 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 46 outputs
Altmetric has tracked 22,660,862 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
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