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Using random walks to identify cancer-associated modules in expression data

Overview of attention for article published in BioData Mining, October 2013
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Mentioned by

twitter
1 tweeter

Citations

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29 Dimensions

Readers on

mendeley
58 Mendeley
citeulike
2 CiteULike
Summary Twitter Dimensions citations
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Title
Using random walks to identify cancer-associated modules in expression data
Published in
BioData Mining, October 2013
DOI 10.1186/1756-0381-6-17
Pubmed ID
24128261
Authors

Deanna Petrochilos, Ali Shojaie, John Gennari, Neil Abernethy

Abstract

The etiology of cancer involves a complex series of genetic and environmental conditions. To better represent and study the intricate genetics of cancer onset and progression, we construct a network of biological interactions to search for groups of genes that compose cancer-related modules. Three cancer expression datasets are investigated to prioritize genes and interactions associated with cancer outcomes. Using a graph-based approach to search for communities of phenotype-related genes in microarray data, we find modules of genes associated with cancer phenotypes in a weighted interaction network.

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

Geographical breakdown

Country Count As %
Germany 1 2%
Taiwan 1 2%
Brazil 1 2%
Unknown 55 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 26%
Student > Ph. D. Student 12 21%
Student > Master 12 21%
Student > Doctoral Student 4 7%
Student > Bachelor 3 5%
Other 8 14%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 31%
Computer Science 12 21%
Biochemistry, Genetics and Molecular Biology 6 10%
Medicine and Dentistry 5 9%
Engineering 4 7%
Other 7 12%
Unknown 6 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 15 October 2013.
All research outputs
#18,349,805
of 22,725,280 outputs
Outputs from BioData Mining
#259
of 307 outputs
Outputs of similar age
#156,973
of 210,770 outputs
Outputs of similar age from BioData Mining
#5
of 6 outputs
Altmetric has tracked 22,725,280 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 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one is in the 6th percentile – i.e., 6% 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 210,770 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.

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