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Attention Score in Context
| Title |
Evaluation of function predictions by PFP, ESG, and PSI-BLAST for moonlighting proteins
|
|---|---|
| Published in |
BMC Proceedings, November 2012
|
| DOI | 10.1186/1753-6561-6-s7-s5 |
| Pubmed ID | |
| Authors |
Ishita K Khan, Meghana Chitale, Catherine Rayon, Daisuke Kihara |
| Abstract |
Advancements in function prediction algorithms are enabling large scale computational annotation for newly sequenced genomes. With the increase in the number of functionally well characterized proteins it has been observed that there are many proteins involved in more than one function. These proteins characterized as moonlighting proteins show varied functional behavior depending on the cell type, localization in the cell, oligomerization, multiple binding sites, etc. The functional diversity shown by moonlighting proteins may have significant impact on the traditional sequence based function prediction methods. Here we investigate how well diverse functions of moonlighting proteins can be predicted by some existing function prediction methods. |
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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 40 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 15% |
| Student > Doctoral Student | 5 | 12% |
| Student > Master | 4 | 10% |
| Student > Bachelor | 4 | 10% |
| Lecturer | 3 | 7% |
| Other | 10 | 24% |
| Unknown | 9 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 27% |
| Biochemistry, Genetics and Molecular Biology | 8 | 20% |
| Business, Management and Accounting | 4 | 10% |
| Arts and Humanities | 3 | 7% |
| Unspecified | 1 | 2% |
| Other | 2 | 5% |
| Unknown | 12 | 29% |
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 14 November 2012.
All research outputs
#15,692,595
of 23,318,744 outputs
Outputs from BMC Proceedings
#216
of 379 outputs
Outputs of similar age
#114,304
of 180,668 outputs
Outputs of similar age from BMC Proceedings
#4
of 7 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 379 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 31st percentile – i.e., 31% 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 180,668 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.