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Attention Score in Context
| Title |
A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests
|
|---|---|
| Published in |
BMC Bioinformatics, July 2009
|
| DOI | 10.1186/1471-2105-10-209 |
| Pubmed ID | |
| Authors |
Antonio Carvajal-Rodríguez, Jacobo de Uña-Alvarez, Emilio Rolán-Alvarez |
| Abstract |
The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test. |
X Demographics
The data shown below were collected from the profile of 1 X user 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 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 5% |
| United Kingdom | 2 | 2% |
| Norway | 1 | <1% |
| Sweden | 1 | <1% |
| Switzerland | 1 | <1% |
| South Africa | 1 | <1% |
| Spain | 1 | <1% |
| Denmark | 1 | <1% |
| Unknown | 97 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 31 | 28% |
| Researcher | 28 | 25% |
| Professor | 11 | 10% |
| Student > Master | 10 | 9% |
| Professor > Associate Professor | 6 | 5% |
| Other | 15 | 14% |
| Unknown | 9 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 61 | 55% |
| Biochemistry, Genetics and Molecular Biology | 10 | 9% |
| Medicine and Dentistry | 6 | 5% |
| Mathematics | 5 | 5% |
| Computer Science | 4 | 4% |
| Other | 14 | 13% |
| Unknown | 10 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 04 October 2022.
All research outputs
#2,927,224
of 23,476,369 outputs
Outputs from BMC Bioinformatics
#985
of 7,394 outputs
Outputs of similar age
#11,001
of 111,568 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 33 outputs
Altmetric has tracked 23,476,369 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 7,394 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 86% 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 111,568 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 90% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.