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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Multi-scale RNA comparison based on RNA triple vector curve representation
|
|---|---|
| Published in |
BMC Bioinformatics, October 2012
|
| DOI | 10.1186/1471-2105-13-280 |
| Pubmed ID | |
| Authors |
Ying Li, Ming Duan, Yanchun Liang |
| Abstract |
In recent years, the important functional roles of RNAs in biological processes have been repeatedly demonstrated. Computing the similarity between two RNAs contributes to better understanding the functional relationship between them. But due to the long-range correlations of RNA, many efficient methods of detecting protein similarity do not work well. In order to comprehensively understand the RNA's function, the better similarity measure among RNAs should be designed to consider their structure features (base pairs). Current methods for RNA comparison could be generally classified into alignment-based and alignment-free. |
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 % |
|---|---|---|
| France | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 6% |
| Egypt | 1 | 6% |
| Unknown | 15 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 41% |
| Student > Ph. D. Student | 2 | 12% |
| Professor | 2 | 12% |
| Student > Master | 2 | 12% |
| Professor > Associate Professor | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 3 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 35% |
| Agricultural and Biological Sciences | 4 | 24% |
| Biochemistry, Genetics and Molecular Biology | 3 | 18% |
| Unknown | 4 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 04 November 2012.
All research outputs
#3,666,805
of 22,684,168 outputs
Outputs from BMC Bioinformatics
#1,391
of 7,252 outputs
Outputs of similar age
#27,467
of 183,634 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 108 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,252 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 done well, scoring higher than 80% 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 183,634 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.