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Mendeley readers
Attention Score in Context
| Title |
A classification approach for genotyping viral sequences based on multidimensional scaling and linear discriminant analysis
|
|---|---|
| Published in |
BMC Bioinformatics, August 2010
|
| DOI | 10.1186/1471-2105-11-434 |
| Pubmed ID | |
| Authors |
Jiwoong Kim, Yongju Ahn, Kichan Lee, Sung Hee Park, Sangsoo Kim |
| Abstract |
Accurate classification into genotypes is critical in understanding evolution of divergent viruses. Here we report a new approach, MuLDAS, which classifies a query sequence based on the statistical genotype models learned from the known sequences. Thus, MuLDAS utilizes full spectra of well characterized sequences as references, typically of an order of hundreds, in order to estimate the significance of each genotype assignment. |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 3% |
| France | 1 | 3% |
| Korea, Republic of | 1 | 3% |
| Australia | 1 | 3% |
| Brazil | 1 | 3% |
| Iran, Islamic Republic of | 1 | 3% |
| China | 1 | 3% |
| Spain | 1 | 3% |
| United States | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 26 | 74% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 37% |
| Student > Ph. D. Student | 11 | 31% |
| Student > Postgraduate | 2 | 6% |
| Professor > Associate Professor | 2 | 6% |
| Student > Bachelor | 1 | 3% |
| Other | 5 | 14% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 46% |
| Computer Science | 5 | 14% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Nursing and Health Professions | 2 | 6% |
| Mathematics | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 3 | 9% |
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 17 February 2015.
All research outputs
#4,171,774
of 22,792,160 outputs
Outputs from BMC Bioinformatics
#1,610
of 7,280 outputs
Outputs of similar age
#17,435
of 94,713 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 45 outputs
Altmetric has tracked 22,792,160 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,280 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 done well, scoring higher than 77% 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 94,713 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 80% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.