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Mendeley readers
Attention Score in Context
| Title |
Evaluation of genomic island predictors using a comparative genomics approach
|
|---|---|
| Published in |
BMC Bioinformatics, August 2008
|
| DOI | 10.1186/1471-2105-9-329 |
| Pubmed ID | |
| Authors |
Morgan GI Langille, William WL Hsiao, Fiona SL Brinkman |
| Abstract |
Genomic islands (GIs) are clusters of genes in prokaryotic genomes of probable horizontal origin. GIs are disproportionately associated with microbial adaptations of medical or environmental interest. Recently, multiple programs for automated detection of GIs have been developed that utilize sequence composition characteristics, such as G+C ratio and dinucleotide bias. To robustly evaluate the accuracy of such methods, we propose that a dataset of GIs be constructed using criteria that are independent of sequence composition-based analysis approaches. |
Mendeley readers
The data shown below were compiled from readership statistics for 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 3% |
| Canada | 3 | 1% |
| Germany | 2 | <1% |
| United Kingdom | 2 | <1% |
| France | 1 | <1% |
| Brazil | 1 | <1% |
| Chile | 1 | <1% |
| India | 1 | <1% |
| Sweden | 1 | <1% |
| Other | 4 | 2% |
| Unknown | 222 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 64 | 26% |
| Researcher | 48 | 20% |
| Student > Master | 34 | 14% |
| Student > Bachelor | 29 | 12% |
| Professor | 8 | 3% |
| Other | 25 | 10% |
| Unknown | 37 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 131 | 53% |
| Biochemistry, Genetics and Molecular Biology | 29 | 12% |
| Computer Science | 13 | 5% |
| Immunology and Microbiology | 13 | 5% |
| Environmental Science | 5 | 2% |
| Other | 13 | 5% |
| Unknown | 41 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 19 November 2008.
All research outputs
#5,651,845
of 22,705,019 outputs
Outputs from BMC Bioinformatics
#2,108
of 7,254 outputs
Outputs of similar age
#23,578
of 82,322 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 31 outputs
Altmetric has tracked 22,705,019 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,254 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 gotten more attention than average, scoring higher than 70% 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 82,322 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 31 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 58% of its contemporaries.