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X Demographics
Mendeley readers
Attention Score in Context
| Title |
FreeContact: fast and free software for protein contact prediction from residue co-evolution
|
|---|---|
| Published in |
BMC Bioinformatics, March 2014
|
| DOI | 10.1186/1471-2105-15-85 |
| Pubmed ID | |
| Authors |
László Kaján, Thomas A Hopf, Matúš Kalaš, Debora S Marks, Burkhard Rost |
| Abstract |
20 years of improved technology and growing sequences now renders residue-residue contact constraints in large protein families through correlated mutations accurate enough to drive de novo predictions of protein three-dimensional structure. The method EVfold broke new ground using mean-field Direct Coupling Analysis (EVfold-mfDCA); the method PSICOV applied a related concept by estimating a sparse inverse covariance matrix. Both methods (EVfold-mfDCA and PSICOV) are publicly available, but both require too much CPU time for interactive applications. On top, EVfold-mfDCA depends on proprietary software. |
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 % |
|---|---|---|
| Germany | 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 160 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 4 | 3% |
| Canada | 3 | 2% |
| United Kingdom | 2 | 1% |
| Brazil | 1 | <1% |
| Argentina | 1 | <1% |
| Greece | 1 | <1% |
| Philippines | 1 | <1% |
| Unknown | 147 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 41 | 26% |
| Researcher | 39 | 24% |
| Student > Master | 22 | 14% |
| Student > Bachelor | 14 | 9% |
| Student > Doctoral Student | 8 | 5% |
| Other | 25 | 16% |
| Unknown | 11 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 57 | 36% |
| Biochemistry, Genetics and Molecular Biology | 45 | 28% |
| Computer Science | 22 | 14% |
| Physics and Astronomy | 7 | 4% |
| Chemistry | 4 | 3% |
| Other | 12 | 8% |
| Unknown | 13 | 8% |
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 18 April 2014.
All research outputs
#17,716,357
of 22,749,166 outputs
Outputs from BMC Bioinformatics
#5,925
of 7,268 outputs
Outputs of similar age
#155,135
of 224,560 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 100 outputs
Altmetric has tracked 22,749,166 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,268 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 13th percentile – i.e., 13% 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 224,560 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 100 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.