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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, January 2013
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1 tweeter

Citations

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20 Dimensions

Readers on

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51 Mendeley
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4 CiteULike
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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, January 2013
DOI 10.1186/1471-2164-14-440
Pubmed ID
Authors

Tae 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 50 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 29%
Researcher 8 16%
Student > Master 8 16%
Other 5 10%
Student > Bachelor 3 6%
Other 7 14%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 35%
Computer Science 10 20%
Biochemistry, Genetics and Molecular Biology 7 14%
Medicine and Dentistry 4 8%
Mathematics 2 4%
Other 5 10%
Unknown 5 10%

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
#9,906,322
of 12,373,620 outputs
Outputs from BMC Genomics
#5,682
of 7,313 outputs
Outputs of similar age
#101,911
of 148,199 outputs
Outputs of similar age from BMC Genomics
#27
of 30 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. 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 148,199 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.