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PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes

Overview of attention for article published in BMC Medical Genomics, May 2014
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Title
PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes
Published in
BMC Medical Genomics, May 2014
DOI 10.1186/1755-8794-7-22
Pubmed ID
Authors

Yannis J Trakadis, Caroline Buote, Jean-François Therriault, Pierre-Étienne Jacques, Hugo Larochelle, Sébastien Lévesque

Abstract

We propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test.Twenty test-patients with varied syndromes were selected from the literature. For each patient, the mutation, phenotypic data, and genetic diagnosis were available. Next, control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient's phenotype and all variations present in the corresponding exome. The physician did not have access to the individual mutations. PhenoVar filtered the search using a cut-off phenotypic match threshold to prevent undesired discovery of incidental findings and ranked the OMIM entries according to diagnostic score.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
France 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 24%
Student > Master 7 15%
Student > Bachelor 5 11%
Professor > Associate Professor 5 11%
Student > Ph. D. Student 3 7%
Other 5 11%
Unknown 10 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 20%
Medicine and Dentistry 9 20%
Agricultural and Biological Sciences 8 17%
Engineering 4 9%
Computer Science 4 9%
Other 1 2%
Unknown 11 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 29 July 2014.
All research outputs
#18,375,064
of 22,758,963 outputs
Outputs from BMC Medical Genomics
#860
of 1,222 outputs
Outputs of similar age
#164,015
of 227,160 outputs
Outputs of similar age from BMC Medical Genomics
#17
of 22 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,222 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 227,160 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.