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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Ensemble-based prediction of RNA secondary structures
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-139 |
| Pubmed ID | |
| Authors |
Nima Aghaeepour, Holger H Hoos |
| Abstract |
Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 5% |
| Unknown | 39 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 24% |
| Researcher | 9 | 22% |
| Student > Doctoral Student | 5 | 12% |
| Student > Master | 4 | 10% |
| Student > Bachelor | 3 | 7% |
| Other | 7 | 17% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 15 | 37% |
| Agricultural and Biological Sciences | 9 | 22% |
| Computer Science | 7 | 17% |
| Physics and Astronomy | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 3 | 7% |
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 26 April 2013.
All research outputs
#17,687,135
of 22,708,120 outputs
Outputs from BMC Bioinformatics
#5,917
of 7,256 outputs
Outputs of similar age
#139,659
of 194,081 outputs
Outputs of similar age from BMC Bioinformatics
#104
of 123 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,256 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 13th percentile – i.e., 13% 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 194,081 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.