| Title |
Clinical implications and considerations for evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines
|
|---|---|
| Published in |
Genome Medicine, December 2017
|
| DOI | 10.1186/s13073-017-0508-z |
| Pubmed ID | |
| Authors |
Lora J. H. Bean, Madhuri R. Hegde |
| Abstract |
Clinical genetics laboratories have recently adopted guidelines for the interpretation of sequence variants set by the American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP). The use of in silico algorithms to predict whether amino acid substitutions result in human disease is inconsistent across clinical laboratories. The clinical genetics community must carefully consider how in silico predictions can be incorporated into variant interpretation in clinical practice.Please see related Research article: https://doi.org/10.1186/s13059-017-1353-5. |
X Demographics
The data shown below were collected from the profiles of 12 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 | 3 | 25% |
| United States | 2 | 17% |
| Switzerland | 1 | 8% |
| Spain | 1 | 8% |
| Taiwan | 1 | 8% |
| Unknown | 4 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 67% |
| Scientists | 4 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 74 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 26% |
| Other | 14 | 19% |
| Student > Ph. D. Student | 9 | 12% |
| Student > Master | 7 | 9% |
| Student > Doctoral Student | 5 | 7% |
| Other | 10 | 14% |
| Unknown | 10 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 25 | 34% |
| Medicine and Dentistry | 13 | 18% |
| Agricultural and Biological Sciences | 12 | 16% |
| Computer Science | 4 | 5% |
| Unspecified | 1 | 1% |
| Other | 3 | 4% |
| Unknown | 16 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 20 April 2018.
All research outputs
#4,520,837
of 23,012,811 outputs
Outputs from Genome Medicine
#867
of 1,448 outputs
Outputs of similar age
#98,430
of 439,953 outputs
Outputs of similar age from Genome Medicine
#22
of 32 outputs
Altmetric has tracked 23,012,811 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,448 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one is in the 40th percentile – i.e., 40% 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 439,953 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 77% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.