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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints
|
|---|---|
| Published in |
BMC Bioinformatics, September 2006
|
| DOI | 10.1186/1471-2105-7-400 |
| Pubmed ID | |
| Authors |
Robin D Dowell, Sean R Eddy |
| Abstract |
We are interested in the problem of predicting secondary structure for small sets of homologous RNAs, by incorporating limited comparative sequence information into an RNA folding model. The Sankoff algorithm for simultaneous RNA folding and alignment is a basis for approaches to this problem. There are two open problems in applying a Sankoff algorithm: development of a good unified scoring system for alignment and folding and development of practical heuristics for dealing with the computational complexity of the algorithm. |
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 % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| France | 2 | 3% |
| Italy | 1 | 1% |
| Germany | 1 | 1% |
| Argentina | 1 | 1% |
| Brazil | 1 | 1% |
| Greece | 1 | 1% |
| China | 1 | 1% |
| Unknown | 68 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 29% |
| Researcher | 15 | 19% |
| Professor > Associate Professor | 9 | 11% |
| Student > Master | 9 | 11% |
| Student > Bachelor | 6 | 8% |
| Other | 11 | 14% |
| Unknown | 6 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 36 | 46% |
| Computer Science | 13 | 16% |
| Biochemistry, Genetics and Molecular Biology | 9 | 11% |
| Mathematics | 2 | 3% |
| Immunology and Microbiology | 2 | 3% |
| Other | 8 | 10% |
| Unknown | 9 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 06 March 2012.
All research outputs
#6,196,094
of 22,663,969 outputs
Outputs from BMC Bioinformatics
#2,380
of 7,246 outputs
Outputs of similar age
#20,381
of 66,863 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 37 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,246 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 66% 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 66,863 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 69% of its contemporaries.
We're also able to compare this research output to 37 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 72% of its contemporaries.