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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures
|
|---|---|
| Published in |
BMC Bioinformatics, May 2014
|
| DOI | 10.1186/1471-2105-15-147 |
| Pubmed ID | |
| Authors |
Hosna Jabbari, Anne Condon |
| Abstract |
Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. |
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 % |
|---|---|---|
| Norway | 1 | 25% |
| France | 1 | 25% |
| Austria | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Colombia | 1 | 3% |
| Unknown | 34 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 25% |
| Student > Ph. D. Student | 8 | 22% |
| Student > Master | 6 | 17% |
| Student > Doctoral Student | 3 | 8% |
| Other | 2 | 6% |
| Other | 6 | 17% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 33% |
| Computer Science | 10 | 28% |
| Biochemistry, Genetics and Molecular Biology | 5 | 14% |
| Immunology and Microbiology | 2 | 6% |
| Engineering | 2 | 6% |
| Other | 3 | 8% |
| Unknown | 2 | 6% |
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 18 May 2014.
All research outputs
#14,781,203
of 22,756,196 outputs
Outputs from BMC Bioinformatics
#5,040
of 7,271 outputs
Outputs of similar age
#127,320
of 227,373 outputs
Outputs of similar age from BMC Bioinformatics
#87
of 149 outputs
Altmetric has tracked 22,756,196 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 7,271 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 227,373 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 149 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.