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Computational methods for ab initio detection of microRNAs

Overview of attention for article published in Frontiers in Genetics, January 2012
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Title
Computational methods for ab initio detection of microRNAs
Published in
Frontiers in Genetics, January 2012
DOI 10.3389/fgene.2012.00209
Pubmed ID
Authors

Jens Allmer, Malik Yousef

Abstract

MicroRNAs are small RNA sequences of 18-24 nucleotides in length, which serve as templates to drive post-transcriptional gene silencing. The canonical microRNA pathway starts with transcription from DNA and is followed by processing via the microprocessor complex, yielding a hairpin structure. Which is then exported into the cytosol where it is processed by Dicer and then incorporated into the RNA-induced silencing complex. All of these biogenesis steps add to the overall specificity of miRNA production and effect. Unfortunately, their modes of action are just beginning to be elucidated and therefore computational prediction algorithms cannot model the process but are usually forced to employ machine learning approaches. This work focuses on ab initio prediction methods throughout; and therefore homology-based miRNA detection methods are not discussed. Current ab initio prediction algorithms, their ties to data mining, and their prediction accuracy are detailed.

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

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 %
Turkey 1 2%
Canada 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 22%
Researcher 10 17%
Student > Bachelor 6 10%
Professor > Associate Professor 5 9%
Student > Doctoral Student 4 7%
Other 11 19%
Unknown 9 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 43%
Computer Science 10 17%
Biochemistry, Genetics and Molecular Biology 5 9%
Neuroscience 2 3%
Arts and Humanities 1 2%
Other 6 10%
Unknown 9 16%
Attention Score in Context

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 10 October 2012.
All research outputs
#20,169,675
of 22,681,577 outputs
Outputs from Frontiers in Genetics
#8,512
of 11,741 outputs
Outputs of similar age
#221,189
of 244,101 outputs
Outputs of similar age from Frontiers in Genetics
#195
of 255 outputs
Altmetric has tracked 22,681,577 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,741 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 255 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.