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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines
|
|---|---|
| Published in |
BMC Bioinformatics, April 2014
|
| DOI | 10.1186/1471-2105-15-120 |
| Pubmed ID | |
| Authors |
Renzhi Cao, Zheng Wang, Yiheng Wang, Jianlin Cheng |
| Abstract |
It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 7% |
| Unknown | 28 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 23% |
| Student > Bachelor | 5 | 17% |
| Student > Master | 3 | 10% |
| Lecturer > Senior Lecturer | 2 | 7% |
| Student > Postgraduate | 2 | 7% |
| Other | 5 | 17% |
| Unknown | 6 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 33% |
| Computer Science | 5 | 17% |
| Biochemistry, Genetics and Molecular Biology | 4 | 13% |
| Medicine and Dentistry | 2 | 7% |
| Social Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 7 | 23% |
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 28 April 2014.
All research outputs
#20,228,822
of 22,754,104 outputs
Outputs from BMC Bioinformatics
#6,841
of 7,269 outputs
Outputs of similar age
#193,638
of 227,639 outputs
Outputs of similar age from BMC Bioinformatics
#130
of 137 outputs
Altmetric has tracked 22,754,104 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,269 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 1st percentile – i.e., 1% 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 227,639 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.