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Mendeley readers
Attention Score in Context
| Title |
In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-s3-s2 |
| Pubmed ID | |
| Authors |
Meghana Chitale, Ishita K Khan, Daisuke Kihara |
| Abstract |
Many Automatic Function Prediction (AFP) methods were developed to cope with an increasing growth of the number of gene sequences that are available from high throughput sequencing experiments. To support the development of AFP methods, it is essential to have community wide experiments for evaluating performance of existing AFP methods. Critical Assessment of Function Annotation (CAFA) is one such community experiment. The meeting of CAFA was held as a Special Interest Group (SIG) meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in 2011. Here, we perform a detailed analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in our lab, using the predictions submitted to CAFA. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 31 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 21% |
| Lecturer | 4 | 12% |
| Student > Ph. D. Student | 3 | 9% |
| Student > Doctoral Student | 3 | 9% |
| Librarian | 2 | 6% |
| Other | 6 | 18% |
| Unknown | 8 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 24% |
| Business, Management and Accounting | 7 | 21% |
| Biochemistry, Genetics and Molecular Biology | 4 | 12% |
| Computer Science | 4 | 12% |
| Arts and Humanities | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 6 | 18% |
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 27 March 2013.
All research outputs
#15,692,595
of 23,318,744 outputs
Outputs from BMC Bioinformatics
#5,483
of 7,384 outputs
Outputs of similar age
#122,924
of 194,427 outputs
Outputs of similar age from BMC Bioinformatics
#119
of 159 outputs
Altmetric has tracked 23,318,744 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 7,384 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 194,427 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% 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 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.