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Performance of case-control rare copy number variation annotation in classification of autism

Overview of attention for article published in BMC Medical Genomics, January 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

twitter
3 tweeters

Citations

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10 Dimensions

Readers on

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54 Mendeley
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1 CiteULike
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Title
Performance of case-control rare copy number variation annotation in classification of autism
Published in
BMC Medical Genomics, January 2015
DOI 10.1186/1755-8794-8-s1-s7
Pubmed ID
Authors

Worrawat Engchuan, Kiret Dhindsa, Anath C Lionel, Stephen W Scherer, Jonathan H Chan, Daniele Merico

Abstract

A substantial proportion of Autism Spectrum Disorder (ASD) risk resides in de novo germline and rare inherited genetic variation. In particular, rare copy number variation (CNV) contributes to ASD risk in up to 10% of ASD subjects. Despite the striking degree of genetic heterogeneity, case-control studies have detected specific burden of rare disruptive CNV for neuronal and neurodevelopmental pathways. Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations. We investigated performance of different methods and estimated the percentage of ASD subjects that could be reliably classified based on presumed etiologic CNV they carry.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Tunisia 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 33%
Researcher 8 15%
Student > Master 8 15%
Student > Bachelor 6 11%
Student > Doctoral Student 3 6%
Other 9 17%
Unknown 2 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 22%
Agricultural and Biological Sciences 9 17%
Medicine and Dentistry 7 13%
Neuroscience 5 9%
Psychology 4 7%
Other 9 17%
Unknown 8 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 09 May 2015.
All research outputs
#1,251,862
of 5,067,991 outputs
Outputs from BMC Medical Genomics
#105
of 347 outputs
Outputs of similar age
#50,576
of 150,177 outputs
Outputs of similar age from BMC Medical Genomics
#7
of 10 outputs
Altmetric has tracked 5,067,991 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 347 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 65% 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 150,177 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.