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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DeepPVP: phenotype-based prioritization of causative variants using deep learning
|
|---|---|
| Published in |
BMC Bioinformatics, February 2019
|
| DOI | 10.1186/s12859-019-2633-8 |
| Pubmed ID | |
| Authors |
Imane Boudellioua, Maxat Kulmanov, Paul N. Schofield, Georgios V. Gkoutos, Robert Hoehndorf |
X Demographics
The data shown below were collected from the profiles of 34 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 | 10 | 29% |
| United Kingdom | 3 | 9% |
| India | 2 | 6% |
| Belgium | 2 | 6% |
| France | 1 | 3% |
| Indonesia | 1 | 3% |
| Switzerland | 1 | 3% |
| Mexico | 1 | 3% |
| Sri Lanka | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 11 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 28 | 82% |
| Scientists | 5 | 15% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 148 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 27 | 18% |
| Student > Ph. D. Student | 20 | 14% |
| Student > Master | 20 | 14% |
| Unspecified | 7 | 5% |
| Student > Postgraduate | 7 | 5% |
| Other | 21 | 14% |
| Unknown | 46 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 38 | 26% |
| Computer Science | 20 | 14% |
| Agricultural and Biological Sciences | 12 | 8% |
| Medicine and Dentistry | 7 | 5% |
| Unspecified | 7 | 5% |
| Other | 16 | 11% |
| Unknown | 48 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 28. 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 02 November 2022.
All research outputs
#1,331,677
of 24,721,757 outputs
Outputs from BMC Bioinformatics
#169
of 7,578 outputs
Outputs of similar age
#32,192
of 447,193 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 176 outputs
Altmetric has tracked 24,721,757 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,578 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 97% of its peers.
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 447,193 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 176 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.