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μHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix

Overview of attention for article published in BMC Bioinformatics, September 2013
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Mentioned by

twitter
2 tweeters

Citations

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10 Dimensions

Readers on

mendeley
29 Mendeley
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1 CiteULike
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Title
μHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix
Published in
BMC Bioinformatics, September 2013
DOI 10.1186/1471-2105-14-266
Pubmed ID
Authors

Sushmita Paul, Pradipta Maji

Abstract

The miRNAs, a class of short approximately 22-nucleotide non-coding RNAs, often act post-transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
United States 1 3%
Denmark 1 3%
Unknown 26 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 31%
Student > Ph. D. Student 8 28%
Student > Postgraduate 3 10%
Professor 2 7%
Lecturer 2 7%
Other 3 10%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 31%
Biochemistry, Genetics and Molecular Biology 6 21%
Medicine and Dentistry 5 17%
Computer Science 4 14%
Engineering 3 10%
Other 0 0%
Unknown 2 7%

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 04 September 2013.
All research outputs
#9,479,924
of 12,378,406 outputs
Outputs from BMC Bioinformatics
#3,561
of 4,542 outputs
Outputs of similar age
#99,151
of 157,223 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 67 outputs
Altmetric has tracked 12,378,406 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,542 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% 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 157,223 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.