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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds
|
|---|---|
| Published in |
BMC Bioinformatics, April 2012
|
| DOI | 10.1186/1471-2105-13-s5-s1 |
| Pubmed ID | |
| Authors |
Yingfeng Wang, Amir Manzour, Pooya Shareghi, Timothy I Shaw, Ying-Wai Li, Russell L Malmberg, Liming Cai |
| Abstract |
The computational identification of RNAs in genomic sequences requires the identification of signals of RNA sequences. Shannon base pairing entropy is an indicator for RNA secondary structure fold certainty in detection of structural, non-coding RNAs (ncRNAs). Under the Boltzmann ensemble of secondary structures, the probability of a base pair is estimated from its frequency across all the alternative equilibrium structures. However, such an entropy has yet to deliver the desired performance for distinguishing ncRNAs from random sequences. Developing novel methods to improve the entropy measure performance may result in more effective ncRNA gene finding based on structure detection. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 10% |
| France | 1 | 10% |
| Canada | 1 | 10% |
| Unknown | 7 | 70% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 40% |
| Student > Master | 2 | 20% |
| Student > Ph. D. Student | 1 | 10% |
| Student > Doctoral Student | 1 | 10% |
| Professor > Associate Professor | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 1 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 40% |
| Biochemistry, Genetics and Molecular Biology | 2 | 20% |
| Immunology and Microbiology | 2 | 20% |
| Agricultural and Biological Sciences | 1 | 10% |
| Unknown | 1 | 10% |
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 14 June 2012.
All research outputs
#18,308,895
of 22,668,244 outputs
Outputs from BMC Bioinformatics
#6,285
of 7,247 outputs
Outputs of similar age
#124,581
of 161,636 outputs
Outputs of similar age from BMC Bioinformatics
#77
of 93 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 5th percentile – i.e., 5% 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 161,636 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.