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HGCS: an online tool for prioritizing disease-causing gene variants by biological distance

Overview of attention for article published in BMC Genomics, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
2 news outlets
blogs
1 blog
twitter
7 tweeters

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
69 Mendeley
citeulike
1 CiteULike
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Title
HGCS: an online tool for prioritizing disease-causing gene variants by biological distance
Published in
BMC Genomics, January 2014
DOI 10.1186/1471-2164-15-256
Pubmed ID
Authors

Yuval Itan, Mark Mazel, Benjamin Mazel, Avinash Abhyankar, Patrick Nitschke, Lluis Quintana-Murci, Stephanie Boisson-Dupuis, Bertrand Boisson, Laurent Abel, Shen-Ying Zhang, Jean-Laurent Casanova

Abstract

Identifying the genotypes underlying human disease phenotypes is a fundamental step in human genetics and medicine. High-throughput genomic technologies provide thousands of genetic variants per individual. The causal genes of a specific phenotype are usually expected to be functionally close to each other. According to this hypothesis, candidate genes are picked from high-throughput data on the basis of their biological proximity to core genes - genes already known to be responsible for the phenotype. There is currently no effective gene-centric online interface for this purpose.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
United States 1 1%
Italy 1 1%
Slovenia 1 1%
Unknown 65 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 25%
Student > Master 17 25%
Student > Ph. D. Student 9 13%
Student > Bachelor 8 12%
Other 4 6%
Other 8 12%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 26%
Biochemistry, Genetics and Molecular Biology 17 25%
Medicine and Dentistry 11 16%
Computer Science 6 9%
Immunology and Microbiology 3 4%
Other 5 7%
Unknown 9 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 26 May 2021.
All research outputs
#1,078,138
of 19,467,557 outputs
Outputs from BMC Genomics
#230
of 9,804 outputs
Outputs of similar age
#12,554
of 202,388 outputs
Outputs of similar age from BMC Genomics
#1
of 16 outputs
Altmetric has tracked 19,467,557 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 9,804 research outputs from this source. They receive a mean Attention Score of 4.5. 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 202,388 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 93% of its contemporaries.
We're also able to compare this research output to 16 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 99% of its contemporaries.