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Genome-wide identification of significant aberrations in cancer genome

Overview of attention for article published in BMC Genomics, July 2012
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  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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4 X users

Citations

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32 Dimensions

Readers on

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46 Mendeley
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2 CiteULike
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Title
Genome-wide identification of significant aberrations in cancer genome
Published in
BMC Genomics, July 2012
DOI 10.1186/1471-2164-13-342
Pubmed ID
Authors

Xiguo Yuan, Guoqiang Yu, Xuchu Hou, Ie-Ming Shih, Robert Clarke, Junying Zhang, Eric P Hoffman, Roger R Wang, Zhen Zhang, Yue Wang

Abstract

Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
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 States 3 7%
India 1 2%
Lithuania 1 2%
Denmark 1 2%
United Kingdom 1 2%
Unknown 39 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 33%
Student > Ph. D. Student 10 22%
Student > Bachelor 6 13%
Student > Postgraduate 4 9%
Professor > Associate Professor 3 7%
Other 5 11%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 43%
Medicine and Dentistry 11 24%
Biochemistry, Genetics and Molecular Biology 5 11%
Computer Science 4 9%
Unknown 6 13%
Attention Score in Context

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 14 August 2012.
All research outputs
#12,857,407
of 22,671,366 outputs
Outputs from BMC Genomics
#4,548
of 10,614 outputs
Outputs of similar age
#87,393
of 164,673 outputs
Outputs of similar age from BMC Genomics
#50
of 120 outputs
Altmetric has tracked 22,671,366 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,614 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% 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 164,673 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.