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Mendeley readers
Attention Score in Context
| Title |
miREE: miRNA recognition elements ensemble
|
|---|---|
| Published in |
BMC Bioinformatics, November 2011
|
| DOI | 10.1186/1471-2105-12-454 |
| Pubmed ID | |
| Authors |
Paula H Reyes-Herrera, Elisa Ficarra, Andrea Acquaviva, Enrico Macii |
| Abstract |
Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| Australia | 1 | 33% |
| France | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Mexico | 1 | 1% |
| Italy | 1 | 1% |
| Thailand | 1 | 1% |
| Unknown | 65 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 29% |
| Researcher | 14 | 20% |
| Professor > Associate Professor | 8 | 11% |
| Student > Bachelor | 7 | 10% |
| Student > Doctoral Student | 5 | 7% |
| Other | 12 | 17% |
| Unknown | 4 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 29 | 41% |
| Biochemistry, Genetics and Molecular Biology | 14 | 20% |
| Computer Science | 6 | 9% |
| Medicine and Dentistry | 4 | 6% |
| Engineering | 4 | 6% |
| Other | 8 | 11% |
| Unknown | 5 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 29 November 2011.
All research outputs
#14,722,660
of 22,659,164 outputs
Outputs from BMC Bioinformatics
#5,027
of 7,236 outputs
Outputs of similar age
#158,356
of 239,474 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 111 outputs
Altmetric has tracked 22,659,164 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% 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 239,474 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 111 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.