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Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers

Overview of attention for article published in BMC Genomics, July 2013
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1 X user

Citations

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53 Mendeley
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Title
Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers
Published in
BMC Genomics, July 2013
DOI 10.1186/1471-2164-14-440
Pubmed ID
Authors

Tae Hyun Hwang, Gowtham Atluri, Rui Kuang, Vipin Kumar, Timothy Starr, Kevin AT Silverstein, Peter M Haverty, Zemin Zhang, Jinfeng Liu

Abstract

Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer.

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The data shown below were collected from the profile of 1 X user 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 28%
Researcher 8 15%
Student > Master 8 15%
Other 5 9%
Student > Bachelor 3 6%
Other 7 13%
Unknown 7 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 34%
Computer Science 10 19%
Biochemistry, Genetics and Molecular Biology 7 13%
Medicine and Dentistry 4 8%
Mathematics 2 4%
Other 5 9%
Unknown 7 13%
Attention Score in Context

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 08 July 2013.
All research outputs
#20,656,820
of 25,374,917 outputs
Outputs from BMC Genomics
#8,709
of 11,244 outputs
Outputs of similar age
#157,215
of 206,393 outputs
Outputs of similar age from BMC Genomics
#140
of 186 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 206,393 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 186 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.