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Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Overview of attention for article published in BMC Bioinformatics, July 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
8 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
56 Mendeley
citeulike
3 CiteULike
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Title
Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method
Published in
BMC Bioinformatics, July 2014
DOI 10.1186/1471-2105-15-255
Pubmed ID
Authors

Ting Wang, Jin Gu, Yanda Li

Abstract

MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
United Kingdom 1 2%
Egypt 1 2%
Denmark 1 2%
United States 1 2%
Luxembourg 1 2%
Unknown 50 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 34%
Researcher 14 25%
Student > Master 7 13%
Student > Bachelor 5 9%
Student > Doctoral Student 3 5%
Other 6 11%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 43%
Computer Science 12 21%
Biochemistry, Genetics and Molecular Biology 9 16%
Mathematics 2 4%
Engineering 2 4%
Other 4 7%
Unknown 3 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 13 October 2014.
All research outputs
#4,061,284
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,789
of 5,420 outputs
Outputs of similar age
#49,399
of 193,453 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 8 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 66% 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 193,453 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.