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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Clustering evolving proteins into homologous families
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-120 |
| Pubmed ID | |
| Authors |
Cheong Xin Chan, Maisarah Mahbob, Mark A Ragan |
| Abstract |
Clustering sequences into groups of putative homologs (families) is a critical first step in many areas of comparative biology and bioinformatics. The performance of clustering approaches in delineating biologically meaningful families depends strongly on characteristics of the data, including content bias and degree of divergence. New, highly scalable methods have recently been introduced to cluster the very large datasets being generated by next-generation sequencing technologies. However, there has been little systematic investigation of how characteristics of the data impact the performance of these approaches. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 29% |
| Germany | 1 | 14% |
| China | 1 | 14% |
| Norway | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 3 | 4% |
| United States | 3 | 4% |
| Australia | 1 | 1% |
| Colombia | 1 | 1% |
| Iran, Islamic Republic of | 1 | 1% |
| United Kingdom | 1 | 1% |
| Russia | 1 | 1% |
| Argentina | 1 | 1% |
| Unknown | 66 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 23 | 29% |
| Student > Ph. D. Student | 21 | 27% |
| Student > Master | 10 | 13% |
| Student > Bachelor | 4 | 5% |
| Professor > Associate Professor | 4 | 5% |
| Other | 10 | 13% |
| Unknown | 6 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 47 | 60% |
| Biochemistry, Genetics and Molecular Biology | 10 | 13% |
| Computer Science | 9 | 12% |
| Environmental Science | 1 | 1% |
| Mathematics | 1 | 1% |
| Other | 3 | 4% |
| Unknown | 7 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 09 October 2014.
All research outputs
#6,923,674
of 22,703,044 outputs
Outputs from BMC Bioinformatics
#2,683
of 7,254 outputs
Outputs of similar age
#59,584
of 199,277 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 139 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
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 61% 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 199,277 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 68% of its contemporaries.
We're also able to compare this research output to 139 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 59% of its contemporaries.