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The Human Phenotype Ontology in 2017

Overview of attention for article published in Nucleic Acids Research, November 2016
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About this Attention Score

  • In the top 5% 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 (96th percentile)

Mentioned by

twitter
50 X users
patent
1 patent
facebook
2 Facebook pages
wikipedia
1 Wikipedia page

Citations

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660 Dimensions

Readers on

mendeley
629 Mendeley
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4 CiteULike
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Title
The Human Phenotype Ontology in 2017
Published in
Nucleic Acids Research, November 2016
DOI 10.1093/nar/gkw1039
Pubmed ID
Authors

Sebastian Köhler, Nicole A. Vasilevsky, Mark Engelstad, Erin Foster, Julie McMurry, Ségolène Aymé, Gareth Baynam, Susan M. Bello, Cornelius F. Boerkoel, Kym M. Boycott, Michael Brudno, Orion J. Buske, Patrick F. Chinnery, Valentina Cipriani, Laureen E. Connell, Hugh J.S. Dawkins, Laura E. DeMare, Andrew D. Devereau, Bert B.A. de Vries, Helen V. Firth, Kathleen Freson, Daniel Greene, Ada Hamosh, Ingo Helbig, Courtney Hum, Johanna A. Jähn, Roger James, Roland Krause, Stanley J. F. Laulederkind, Hanns Lochmüller, Gholson J. Lyon, Soichi Ogishima, Annie Olry, Willem H. Ouwehand, Nikolas Pontikos, Ana Rath, Franz Schaefer, Richard H. Scott, Michael Segal, Panagiotis I. Sergouniotis, Richard Sever, Cynthia L. Smith, Volker Straub, Rachel Thompson, Catherine Turner, Ernest Turro, Marijcke W.M. Veltman, Tom Vulliamy, Jing Yu, Julie von Ziegenweidt, Andreas Zankl, Stephan Züchner, Tomasz Zemojtel, Julius O.B. Jacobsen, Tudor Groza, Damian Smedley, Christopher J. Mungall, Melissa Haendel, Peter N. Robinson

Abstract

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

X Demographics

X Demographics

The data shown below were collected from the profiles of 50 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 629 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 <1%
Spain 2 <1%
Germany 1 <1%
Canada 1 <1%
South Africa 1 <1%
United Kingdom 1 <1%
Luxembourg 1 <1%
Unknown 617 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 112 18%
Student > Ph. D. Student 104 17%
Student > Master 77 12%
Student > Bachelor 51 8%
Other 47 7%
Other 106 17%
Unknown 132 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 163 26%
Medicine and Dentistry 100 16%
Agricultural and Biological Sciences 80 13%
Computer Science 63 10%
Neuroscience 16 3%
Other 59 9%
Unknown 148 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 31 May 2022.
All research outputs
#1,032,721
of 24,875,286 outputs
Outputs from Nucleic Acids Research
#552
of 27,489 outputs
Outputs of similar age
#21,193
of 427,927 outputs
Outputs of similar age from Nucleic Acids Research
#13
of 308 outputs
Altmetric has tracked 24,875,286 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 27,489 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. 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 427,927 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 308 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 96% of its contemporaries.