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Advancing data reuse in phyloinformatics using an ontology-driven Semantic Web approach

Overview of attention for article published in BMC Medical Genomics, November 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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6 X users

Citations

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7 Dimensions

Readers on

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27 Mendeley
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1 CiteULike
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Title
Advancing data reuse in phyloinformatics using an ontology-driven Semantic Web approach
Published in
BMC Medical Genomics, November 2013
DOI 10.1186/1755-8794-6-s3-s5
Pubmed ID
Authors

Maryam Panahiazar, Amit P Sheth, Ajith Ranabahu, Rutger A Vos, Jim Leebens-Mack

Abstract

Phylogenetic analyses can resolve historical relationships among genes, organisms or higher taxa. Understanding such relationships can elucidate a wide range of biological phenomena, including, for example, the importance of gene and genome duplications in the evolution of gene function, the role of adaptation as a driver of diversification, or the evolutionary consequences of biogeographic shifts. Phyloinformaticists are developing data standards, databases and communication protocols (e.g. Application Programming Interfaces, APIs) to extend the accessibility of gene trees, species trees, and the metadata necessary to interpret these trees, thus enabling researchers across the life sciences to reuse phylogenetic knowledge. Specifically, Semantic Web technologies are being developed to make phylogenetic knowledge interpretable by web agents, thereby enabling intelligently automated, high-throughput reuse of results generated by phylogenetic research. This manuscript describes an ontology-driven, semantic problem-solving environment for phylogenetic analyses and introduces artefacts that can promote phyloinformatic efforts to promote accessibility of trees and underlying metadata. PhylOnt is an extensible ontology with concepts describing tree types and tree building methodologies including estimation methods, models and programs. In addition we present the PhylAnt platform for annotating scientific articles and NeXML files with PhylOnt concepts. The novelty of this work is the annotation of NeXML files and phylogenetic related documents with PhylOnt Ontology. This approach advances data reuse in phyloinformatics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 7%
Germany 1 4%
Thailand 1 4%
Unknown 23 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 22%
Student > Bachelor 4 15%
Researcher 4 15%
Student > Master 3 11%
Professor 3 11%
Other 3 11%
Unknown 4 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 37%
Computer Science 5 19%
Engineering 5 19%
Social Sciences 2 7%
Business, Management and Accounting 1 4%
Other 1 4%
Unknown 3 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 28 February 2014.
All research outputs
#7,148,094
of 25,371,288 outputs
Outputs from BMC Medical Genomics
#478
of 2,444 outputs
Outputs of similar age
#61,486
of 225,250 outputs
Outputs of similar age from BMC Medical Genomics
#9
of 36 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 2,444 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 80% 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 225,250 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.