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Mendeley readers
Attention Score in Context
Title |
Computational identification of DrosophilamicroRNA genes
|
---|---|
Published in |
Genome Biology, June 2003
|
DOI | 10.1186/gb-2003-4-7-r42 |
Pubmed ID | |
Authors |
Eric C Lai, Pavel Tomancak, Robert W Williams, Gerald M Rubin |
Abstract |
MicroRNAs (miRNAs) are a large family of 21-22 nucleotide non-coding RNAs with presumed post-transcriptional regulatory activity. Most miRNAs were identified by direct cloning of small RNAs, an approach that favors detection of abundant miRNAs. Three observations suggested that miRNA genes might be identified using a computational approach. First, miRNAs generally derive from precursor transcripts of 70-100 nucleotides with extended stem-loop structure. Second, miRNAs are usually highly conserved between the genomes of related species. Third, miRNAs display a characteristic pattern of evolutionary divergence. |
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 % |
---|---|---|
Germany | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 435 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 5 | 1% |
United States | 5 | 1% |
Brazil | 4 | <1% |
Turkey | 2 | <1% |
Germany | 2 | <1% |
Sweden | 2 | <1% |
Spain | 2 | <1% |
Canada | 2 | <1% |
France | 1 | <1% |
Other | 9 | 2% |
Unknown | 401 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 113 | 26% |
Researcher | 93 | 21% |
Student > Bachelor | 43 | 10% |
Student > Master | 42 | 10% |
Professor > Associate Professor | 36 | 8% |
Other | 64 | 15% |
Unknown | 44 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 230 | 53% |
Biochemistry, Genetics and Molecular Biology | 78 | 18% |
Computer Science | 26 | 6% |
Medicine and Dentistry | 16 | 4% |
Engineering | 7 | 2% |
Other | 23 | 5% |
Unknown | 55 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 18 April 2023.
All research outputs
#5,240,751
of 25,374,917 outputs
Outputs from Genome Biology
#2,860
of 4,467 outputs
Outputs of similar age
#9,113
of 52,243 outputs
Outputs of similar age from Genome Biology
#5
of 13 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 35th percentile – i.e., 35% 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 52,243 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 82% of its contemporaries.
We're also able to compare this research output to 13 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 61% of its contemporaries.