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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

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

twitter
3 tweeters
patent
1 patent

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
71 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Tunisia 1 1%
Unknown 70 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 24%
Researcher 9 13%
Student > Bachelor 9 13%
Student > Master 8 11%
Student > Doctoral Student 4 6%
Other 13 18%
Unknown 11 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 17%
Neuroscience 9 13%
Agricultural and Biological Sciences 8 11%
Medicine and Dentistry 8 11%
Psychology 5 7%
Other 12 17%
Unknown 17 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 23 December 2021.
All research outputs
#5,144,165
of 21,385,220 outputs
Outputs from BMC Medical Genomics
#232
of 1,139 outputs
Outputs of similar age
#57,713
of 236,954 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 1 outputs
Altmetric has tracked 21,385,220 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,139 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 79% 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 236,954 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 75% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them