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PhenomeCentral: A Portal for Phenotypic and Genotypic Matchmaking of Patients with Rare Genetic Diseases

Overview of attention for article published in Human Mutation, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
75 Dimensions

Readers on

mendeley
113 Mendeley
citeulike
1 CiteULike
Title
PhenomeCentral: A Portal for Phenotypic and Genotypic Matchmaking of Patients with Rare Genetic Diseases
Published in
Human Mutation, August 2015
DOI 10.1002/humu.22851
Pubmed ID
Authors

Orion J. Buske, Marta Girdea, Sergiu Dumitriu, Bailey Gallinger, Taila Hartley, Heather Trang, Andriy Misyura, Tal Friedman, Chandree Beaulieu, William P. Bone, Amanda E. Links, Nicole L. Washington, Melissa A. Haendel, Peter N. Robinson, Cornelius F. Boerkoel, David Adams, William A. Gahl, Kym M. Boycott, Michael Brudno

Abstract

The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 4%
United States 1 <1%
Iceland 1 <1%
Unknown 107 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 25%
Student > Ph. D. Student 19 17%
Student > Master 16 14%
Other 9 8%
Professor 7 6%
Other 21 19%
Unknown 13 12%
Readers by discipline Count As %
Medicine and Dentistry 27 24%
Biochemistry, Genetics and Molecular Biology 24 21%
Agricultural and Biological Sciences 24 21%
Computer Science 11 10%
Neuroscience 2 2%
Other 4 4%
Unknown 21 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 19 November 2015.
All research outputs
#3,252,682
of 16,581,438 outputs
Outputs from Human Mutation
#378
of 2,467 outputs
Outputs of similar age
#51,123
of 237,633 outputs
Outputs of similar age from Human Mutation
#2
of 24 outputs
Altmetric has tracked 16,581,438 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,467 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 84% 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 237,633 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 24 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 95% of its contemporaries.