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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
|
|---|---|
| Published in |
Genome Biology, August 2016
|
| DOI | 10.1186/s13059-016-1029-6 |
| Pubmed ID | |
| Authors |
Yu Fan, Liu Xi, Daniel S. T. Hughes, Jianjun Zhang, Jianhua Zhang, P. Andrew Futreal, David A. Wheeler, Wenyi Wang |
X Demographics
The data shown below were collected from the profiles of 20 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 | 50% |
| China | 1 | 5% |
| Brazil | 1 | 5% |
| Malaysia | 1 | 5% |
| United Kingdom | 1 | 5% |
| Unknown | 6 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 11 | 55% |
| Members of the public | 8 | 40% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 243 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 240 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 53 | 22% |
| Student > Ph. D. Student | 50 | 21% |
| Student > Master | 25 | 10% |
| Student > Bachelor | 17 | 7% |
| Student > Doctoral Student | 16 | 7% |
| Other | 31 | 13% |
| Unknown | 51 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 81 | 33% |
| Agricultural and Biological Sciences | 42 | 17% |
| Computer Science | 24 | 10% |
| Medicine and Dentistry | 10 | 4% |
| Mathematics | 5 | 2% |
| Other | 22 | 9% |
| Unknown | 59 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 03 April 2017.
All research outputs
#3,194,591
of 26,017,215 outputs
Outputs from Genome Biology
#2,335
of 4,513 outputs
Outputs of similar age
#52,176
of 358,304 outputs
Outputs of similar age from Genome Biology
#32
of 55 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,513 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.7. This one is in the 48th percentile – i.e., 48% 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 358,304 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 84% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.