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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 25% |
| Germany | 1 | 25% |
| Australia | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 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% |
| Unknown | 107 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 24% |
| Researcher | 23 | 20% |
| Student > Bachelor | 17 | 15% |
| Student > Master | 12 | 10% |
| Professor > Associate Professor | 9 | 8% |
| Other | 14 | 12% |
| Unknown | 12 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 58 | 50% |
| Computer Science | 16 | 14% |
| Biochemistry, Genetics and Molecular Biology | 9 | 8% |
| Engineering | 7 | 6% |
| Medicine and Dentistry | 3 | 3% |
| Other | 5 | 4% |
| Unknown | 17 | 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 03 January 2012.
All research outputs
#12,659,757
of 22,660,862 outputs
Outputs from BMC Genomics
#4,377
of 10,612 outputs
Outputs of similar age
#141,483
of 243,633 outputs
Outputs of similar age from BMC Genomics
#125
of 294 outputs
Altmetric has tracked 22,660,862 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 10,612 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 243,633 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 294 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 56% of its contemporaries.