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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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Mentioned by

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1 tweeter

Citations

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19 Dimensions

Readers on

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41 Mendeley
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1 CiteULike
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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
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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 41 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 39 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 29%
Student > Master 7 17%
Professor > Associate Professor 5 12%
Student > Bachelor 4 10%
Student > Ph. D. Student 3 7%
Other 5 12%
Unknown 5 12%
Readers by discipline Count As %
Medicine and Dentistry 9 22%
Agricultural and Biological Sciences 8 20%
Biochemistry, Genetics and Molecular Biology 8 20%
Computer Science 4 10%
Engineering 3 7%
Other 2 5%
Unknown 7 17%

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
#3,075,329
of 4,507,280 outputs
Outputs from BMC Medical Genomics
#230
of 310 outputs
Outputs of similar age
#73,362
of 109,748 outputs
Outputs of similar age from BMC Medical Genomics
#12
of 15 outputs
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So far Altmetric has tracked 310 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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