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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity
|
|---|---|
| Published in |
Genome Medicine, January 2015
|
| DOI | 10.1186/s13073-014-0120-4 |
| Pubmed ID | |
| Authors |
Dace Ruklisa, James S Ware, Roddy Walsh, David J Balding, Stuart A Cook |
| Abstract |
With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation. |
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 % |
|---|---|---|
| United Kingdom | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| United States | 1 | 2% |
| Australia | 1 | 2% |
| Unknown | 53 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 25% |
| Student > Ph. D. Student | 11 | 20% |
| Professor | 5 | 9% |
| Student > Bachelor | 5 | 9% |
| Student > Master | 5 | 9% |
| Other | 9 | 16% |
| Unknown | 7 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 21% |
| Agricultural and Biological Sciences | 12 | 21% |
| Medicine and Dentistry | 9 | 16% |
| Computer Science | 8 | 14% |
| Engineering | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 8 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 22 March 2018.
All research outputs
#6,754,036
of 25,374,647 outputs
Outputs from Genome Medicine
#1,107
of 1,585 outputs
Outputs of similar age
#84,300
of 361,131 outputs
Outputs of similar age from Genome Medicine
#16
of 29 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one is in the 29th percentile – i.e., 29% 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 361,131 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 76% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.