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Attention Score in Context
Title |
A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction
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Published in |
BMC Genomics, May 2014
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DOI | 10.1186/1471-2164-15-348 |
Pubmed ID | |
Authors |
Prashant K Srivastava, Taraka Ramji Moturu, Priyanka Pandey, Ian T Baldwin, Shree P Pandey |
Abstract |
Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 40% |
Spain | 1 | 20% |
France | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 180 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 3 | 2% |
Germany | 1 | <1% |
Malaysia | 1 | <1% |
Portugal | 1 | <1% |
France | 1 | <1% |
Norway | 1 | <1% |
Israel | 1 | <1% |
New Zealand | 1 | <1% |
Thailand | 1 | <1% |
Other | 3 | 2% |
Unknown | 166 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 38 | 21% |
Researcher | 37 | 21% |
Student > Master | 27 | 15% |
Student > Bachelor | 22 | 12% |
Professor | 9 | 5% |
Other | 29 | 16% |
Unknown | 18 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 90 | 50% |
Biochemistry, Genetics and Molecular Biology | 39 | 22% |
Computer Science | 9 | 5% |
Chemistry | 4 | 2% |
Engineering | 3 | 2% |
Other | 8 | 4% |
Unknown | 27 | 15% |
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 19 May 2014.
All research outputs
#12,838,979
of 22,755,127 outputs
Outputs from BMC Genomics
#4,512
of 10,637 outputs
Outputs of similar age
#105,898
of 227,621 outputs
Outputs of similar age from BMC Genomics
#63
of 197 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,637 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 57% 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 227,621 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 53% of its contemporaries.
We're also able to compare this research output to 197 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 67% of its contemporaries.