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X Demographics
Mendeley readers
Attention Score in Context
| Title |
PconsFold: improved contact predictions improve protein models
|
|---|---|
| Published in |
Bioinformatics, August 2014
|
| DOI | 10.1093/bioinformatics/btu458 |
| Pubmed ID | |
| Authors |
Mirco Michel, Sikander Hayat, Marcin J Skwark, Chris Sander, Debora S Marks, Arne Elofsson |
| Abstract |
Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 33% |
| Germany | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
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 % |
|---|---|---|
| Germany | 3 | 3% |
| United Kingdom | 3 | 3% |
| Canada | 2 | 2% |
| Spain | 1 | <1% |
| Greece | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 97 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 42 | 39% |
| Researcher | 23 | 21% |
| Student > Master | 9 | 8% |
| Student > Bachelor | 6 | 6% |
| Student > Postgraduate | 6 | 6% |
| Other | 13 | 12% |
| Unknown | 9 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 42 | 39% |
| Computer Science | 21 | 19% |
| Biochemistry, Genetics and Molecular Biology | 17 | 16% |
| Chemistry | 7 | 6% |
| Physics and Astronomy | 3 | 3% |
| Other | 5 | 5% |
| Unknown | 13 | 12% |
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 29 August 2014.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from Bioinformatics
#10,568
of 12,809 outputs
Outputs of similar age
#148,262
of 247,504 outputs
Outputs of similar age from Bioinformatics
#205
of 230 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 11th percentile – i.e., 11% 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 247,504 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 230 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.