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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

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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8 X users
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1 Facebook page

Citations

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11 Dimensions

Readers on

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57 Mendeley
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 57 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 51 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 33%
Researcher 15 26%
Student > Master 7 12%
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 42%
Computer Science 13 23%
Biochemistry, Genetics and Molecular Biology 8 14%
Engineering 3 5%
Mathematics 2 4%
Other 4 7%
Unknown 3 5%
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 13 October 2014.
All research outputs
#7,444,997
of 22,758,963 outputs
Outputs from BMC Bioinformatics
#3,021
of 7,273 outputs
Outputs of similar age
#73,298
of 228,919 outputs
Outputs of similar age from BMC Bioinformatics
#63
of 132 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 228,919 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 56% of its contemporaries.
We're also able to compare this research output to 132 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 52% of its contemporaries.