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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Protein function prediction by massive integration of evolutionary analyses and multiple data sources
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-s3-s1 |
| Pubmed ID | |
| Authors |
Domenico Cozzetto, Daniel WA Buchan, Kevin Bryson, David T Jones |
| Abstract |
Accurate protein function annotation is a severe bottleneck when utilizing the deluge of high-throughput, next generation sequencing data. Keeping database annotations up-to-date has become a major scientific challenge that requires the development of reliable automatic predictors of protein function. The CAFA experiment provided a unique opportunity to undertake comprehensive 'blind testing' of many diverse approaches for automated function prediction. We report on the methodology we used for this challenge and on the lessons we learnt. |
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 % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 4% |
| France | 1 | <1% |
| Israel | 1 | <1% |
| Brazil | 1 | <1% |
| United Kingdom | 1 | <1% |
| India | 1 | <1% |
| Unknown | 99 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 26% |
| Researcher | 23 | 21% |
| Student > Master | 13 | 12% |
| Professor | 7 | 6% |
| Student > Bachelor | 7 | 6% |
| Other | 18 | 17% |
| Unknown | 12 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 37 | 34% |
| Computer Science | 28 | 26% |
| Biochemistry, Genetics and Molecular Biology | 17 | 16% |
| Engineering | 3 | 3% |
| Unspecified | 2 | 2% |
| Other | 9 | 8% |
| Unknown | 12 | 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 22 April 2013.
All research outputs
#18,336,865
of 22,707,247 outputs
Outputs from BMC Bioinformatics
#6,291
of 7,256 outputs
Outputs of similar age
#146,759
of 192,985 outputs
Outputs of similar age from BMC Bioinformatics
#136
of 159 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,256 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 5th percentile – i.e., 5% 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 192,985 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.