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X Demographics
Mendeley readers
Attention Score in Context
| Title |
RNA-RNA interaction prediction using genetic algorithm
|
|---|---|
| Published in |
Algorithms for Molecular Biology, June 2014
|
| DOI | 10.1186/1748-7188-9-17 |
| Pubmed ID | |
| Authors |
Soheila Montaseri, Fatemeh Zare-Mirakabad, Nasrollah Moghadam-Charkari |
| Abstract |
RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time. |
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 % |
|---|---|---|
| Venezuela, Bolivarian Republic of | 1 | 50% |
| France | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
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 % |
|---|---|---|
| New Caledonia | 1 | 6% |
| Germany | 1 | 6% |
| Brazil | 1 | 6% |
| Unknown | 14 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 24% |
| Professor > Associate Professor | 3 | 18% |
| Researcher | 3 | 18% |
| Student > Ph. D. Student | 2 | 12% |
| Unknown | 5 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 29% |
| Agricultural and Biological Sciences | 4 | 24% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Chemistry | 1 | 6% |
| Unknown | 6 | 35% |
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 31 July 2014.
All research outputs
#17,722,431
of 22,757,541 outputs
Outputs from Algorithms for Molecular Biology
#173
of 264 outputs
Outputs of similar age
#154,633
of 227,015 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 4 outputs
Altmetric has tracked 22,757,541 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 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 25th percentile – i.e., 25% 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 227,015 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.