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Mendeley readers
Attention Score in Context
| Title |
MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences
|
|---|---|
| Published in |
BMC Bioinformatics, April 2011
|
| DOI | 10.1186/1471-2105-12-107 |
| Pubmed ID | |
| Authors |
Yonggan Wu, Bo Wei, Haizhou Liu, Tianxian Li, Simon Rayner |
| Abstract |
MicroRNAs are a family of ~22 nt small RNAs that can regulate gene expression at the post-transcriptional level. Identification of these molecules and their targets can aid understanding of regulatory processes. Recently, HTS has become a common identification method but there are two major limitations associated with the technique. Firstly, the method has low efficiency, with typically less than 1 in 10,000 sequences representing miRNA reads and secondly the method preferentially targets highly expressed miRNAs. If sequences are available, computational methods can provide a screening step to investigate the value of an HTS study and aid interpretation of results. However, current methods can only predict miRNAs for short fragments and have usually been trained against small datasets which don't always reflect the diversity of these molecules. |
Mendeley readers
The data shown below were compiled from readership statistics for 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| Brazil | 2 | 1% |
| Canada | 2 | 1% |
| Spain | 2 | 1% |
| United Kingdom | 2 | 1% |
| France | 1 | <1% |
| Austria | 1 | <1% |
| Turkey | 1 | <1% |
| India | 1 | <1% |
| Other | 6 | 4% |
| Unknown | 128 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 40 | 27% |
| Student > Ph. D. Student | 36 | 24% |
| Student > Master | 19 | 13% |
| Professor > Associate Professor | 10 | 7% |
| Student > Bachelor | 9 | 6% |
| Other | 23 | 15% |
| Unknown | 13 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 73 | 49% |
| Biochemistry, Genetics and Molecular Biology | 32 | 21% |
| Computer Science | 13 | 9% |
| Engineering | 6 | 4% |
| Medicine and Dentistry | 3 | 2% |
| Other | 6 | 4% |
| Unknown | 17 | 11% |
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 11 August 2012.
All research outputs
#20,167,959
of 22,679,690 outputs
Outputs from BMC Bioinformatics
#6,820
of 7,251 outputs
Outputs of similar age
#102,008
of 109,047 outputs
Outputs of similar age from BMC Bioinformatics
#62
of 64 outputs
Altmetric has tracked 22,679,690 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,251 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 1st percentile – i.e., 1% 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 109,047 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.