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Genome-wide algorithm for detecting CNV associations with diseases

Overview of attention for article published in BMC Bioinformatics, January 2011
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

blogs
1 blog
twitter
5 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
41 Mendeley
citeulike
3 CiteULike
connotea
2 Connotea
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Title
Genome-wide algorithm for detecting CNV associations with diseases
Published in
BMC Bioinformatics, January 2011
DOI 10.1186/1471-2105-12-331
Pubmed ID
Authors

Yaji Xu, Bo Peng, Yunxin Fu, Christopher I Amos

Abstract

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
Germany 1 2%
Sweden 1 2%
France 1 2%
Mexico 1 2%
Belgium 1 2%
United States 1 2%
Poland 1 2%
Unknown 32 78%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 32%
Student > Ph. D. Student 11 27%
Professor > Associate Professor 3 7%
Student > Master 3 7%
Other 2 5%
Other 7 17%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 59%
Medicine and Dentistry 6 15%
Biochemistry, Genetics and Molecular Biology 5 12%
Mathematics 2 5%
Business, Management and Accounting 1 2%
Other 1 2%
Unknown 2 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 16 August 2011.
All research outputs
#1,350,772
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#564
of 4,576 outputs
Outputs of similar age
#10,484
of 86,450 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 19 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 87% 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 86,450 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 87% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.