| Title |
VarSight: prioritizing clinically reported variants with binary classification algorithms
|
|---|---|
| Published in |
BMC Bioinformatics, October 2019
|
| DOI | 10.1186/s12859-019-3026-8 |
| Pubmed ID | |
| Authors |
James M. Holt, Brandon Wilk, Camille L. Birch, Donna M. Brown, Manavalan Gajapathy, Alexander C. Moss, Nadiya Sosonkina, Melissa A. Wilk, Julie A. Anderson, Jeremy M. Harris, Jacob M. Kelly, Fariba Shaterferdosian, Angelina E. Uno-Antonison, Arthur Weborg, Elizabeth A. Worthey |
X Demographics
The data shown below were collected from the profiles of 24 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 | 9 | 38% |
| Spain | 2 | 8% |
| United Kingdom | 2 | 8% |
| India | 1 | 4% |
| Germany | 1 | 4% |
| Unknown | 9 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 12 | 50% |
| Members of the public | 11 | 46% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 97 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 13% |
| Student > Master | 11 | 11% |
| Professor | 11 | 11% |
| Other | 10 | 10% |
| Student > Ph. D. Student | 9 | 9% |
| Other | 18 | 19% |
| Unknown | 25 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 33 | 34% |
| Medicine and Dentistry | 8 | 8% |
| Computer Science | 6 | 6% |
| Agricultural and Biological Sciences | 5 | 5% |
| Engineering | 4 | 4% |
| Other | 14 | 14% |
| Unknown | 27 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 08 January 2020.
All research outputs
#2,429,403
of 24,597,084 outputs
Outputs from BMC Bioinformatics
#637
of 7,559 outputs
Outputs of similar age
#50,683
of 359,913 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 135 outputs
Altmetric has tracked 24,597,084 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,559 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 91% 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 359,913 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.