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Mendeley readers
Attention Score in Context
| Title |
Discovering co-occurring patterns and their biological significance in protein families
|
|---|---|
| Published in |
BMC Bioinformatics, November 2014
|
| DOI | 10.1186/1471-2105-15-s12-s2 |
| Pubmed ID | |
| Authors |
En-Shiun Annie Lee, Sanderz Fung, Ho-Yin Sze-To, Andrew K C Wong |
| Abstract |
The large influx of biological sequences poses the importance of identifying and correlating conserved regions in homologous sequences to acquire valuable biological knowledge. These conserved regions contain statistically significant residue associations as sequence patterns. Thus, patterns from two conserved regions co-occurring frequently on the same sequences are inferred to have joint functionality. A method for finding conserved regions in protein families with frequent co-occurrence patterns is proposed. The biological significance of the discovered clusters of conserved regions with co-occurrences patterns can be validated by their three-dimensional closeness of amino acids and the biological functionality found in those regions as supported by published work. |
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 % |
|---|---|---|
| Unknown | 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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | 7% |
| Unknown | 13 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 3 | 21% |
| Student > Ph. D. Student | 2 | 14% |
| Researcher | 2 | 14% |
| Student > Bachelor | 1 | 7% |
| Professor > Associate Professor | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 5 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 29% |
| Agricultural and Biological Sciences | 3 | 21% |
| Biochemistry, Genetics and Molecular Biology | 2 | 14% |
| Unknown | 5 | 36% |
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 05 December 2014.
All research outputs
#15,311,799
of 22,772,779 outputs
Outputs from BMC Bioinformatics
#5,373
of 7,276 outputs
Outputs of similar age
#153,152
of 262,799 outputs
Outputs of similar age from BMC Bioinformatics
#101
of 144 outputs
Altmetric has tracked 22,772,779 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 7,276 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 262,799 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 144 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.