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Employing machine learning for reliable miRNA target identification in plants

Overview of attention for article published in BMC Genomics, December 2011
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
4 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
99 Mendeley
citeulike
8 CiteULike
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Title
Employing machine learning for reliable miRNA target identification in plants
Published in
BMC Genomics, December 2011
DOI 10.1186/1471-2164-12-636
Pubmed ID
Authors

Ashwani Jha, Ravi Shankar

Abstract

miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 1%
Germany 1 1%
Norway 1 1%
Italy 1 1%
Sweden 1 1%
United Kingdom 1 1%
Taiwan 1 1%
United States 1 1%
Poland 1 1%
Other 0 0%
Unknown 90 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 26%
Researcher 24 24%
Student > Bachelor 14 14%
Student > Master 12 12%
Professor > Associate Professor 9 9%
Other 10 10%
Unknown 4 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 58%
Computer Science 14 14%
Biochemistry, Genetics and Molecular Biology 9 9%
Engineering 6 6%
Medicine and Dentistry 2 2%
Other 3 3%
Unknown 8 8%

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 03 January 2012.
All research outputs
#6,432,612
of 12,373,620 outputs
Outputs from BMC Genomics
#3,051
of 7,313 outputs
Outputs of similar age
#87,742
of 222,683 outputs
Outputs of similar age from BMC Genomics
#261
of 580 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% 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 has gotten more attention than average, scoring higher than 56% 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 222,683 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 59% of its contemporaries.
We're also able to compare this research output to 580 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 53% of its contemporaries.