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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Status quo of annotation of human disease variants
|
|---|---|
| Published in |
BMC Bioinformatics, December 2013
|
| DOI | 10.1186/1471-2105-14-352 |
| Pubmed ID | |
| Authors |
Hanka Venselaar, Franscesca Camilli, Shima Gholizadeh, Marlou Snelleman, Han G Brunner, Gert Vriend |
| Abstract |
The ever on-going technical developments in Next Generation Sequencing have led to an increase in detected disease related mutations. Many bioinformatics approaches exist to analyse these variants, and of those the methods that use 3D structure information generally outperform those that do not use this information. 3D structure information today is available for about twenty percent of the human exome, and homology modelling can double that fraction. This percentage is rapidly increasing so that we can expect to analyse the majority of all human exome variants in the near future using protein structure information. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 25% |
| Israel | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 7% |
| Germany | 2 | 5% |
| Spain | 2 | 5% |
| France | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 34 | 79% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 28% |
| Other | 7 | 16% |
| Student > Ph. D. Student | 7 | 16% |
| Student > Master | 7 | 16% |
| Student > Bachelor | 3 | 7% |
| Other | 3 | 7% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 47% |
| Biochemistry, Genetics and Molecular Biology | 4 | 9% |
| Computer Science | 4 | 9% |
| Medicine and Dentistry | 3 | 7% |
| Mathematics | 1 | 2% |
| Other | 5 | 12% |
| Unknown | 6 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 11 March 2014.
All research outputs
#15,330,390
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#5,139
of 7,400 outputs
Outputs of similar age
#189,756
of 310,831 outputs
Outputs of similar age from BMC Bioinformatics
#63
of 107 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 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 26th percentile – i.e., 26% 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 310,831 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.