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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Word correlation matrices for protein sequence analysis and remote homology detection
|
|---|---|
| Published in |
BMC Bioinformatics, June 2008
|
| DOI | 10.1186/1471-2105-9-259 |
| Pubmed ID | |
| Authors |
Thomas Lingner, Peter Meinicke |
| Abstract |
Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive. |
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 % |
|---|---|---|
| Canada | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 11% |
| United States | 1 | 5% |
| Sweden | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 14 | 74% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 32% |
| Student > Doctoral Student | 4 | 21% |
| Other | 2 | 11% |
| Student > Ph. D. Student | 2 | 11% |
| Professor | 2 | 11% |
| Other | 2 | 11% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 58% |
| Computer Science | 4 | 21% |
| Earth and Planetary Sciences | 1 | 5% |
| Medicine and Dentistry | 1 | 5% |
| Unknown | 2 | 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 04 September 2014.
All research outputs
#15,305,567
of 22,763,032 outputs
Outputs from BMC Bioinformatics
#5,373
of 7,273 outputs
Outputs of similar age
#69,846
of 82,390 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 44 outputs
Altmetric has tracked 22,763,032 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 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 18th percentile – i.e., 18% 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 82,390 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.