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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Filtering, FDR and power
|
|---|---|
| Published in |
BMC Bioinformatics, September 2010
|
| DOI | 10.1186/1471-2105-11-450 |
| Pubmed ID | |
| Authors |
Maarten van Iterson, Judith M Boer, Renée X Menezes |
| Abstract |
In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 6% |
| Germany | 2 | 1% |
| United Kingdom | 2 | 1% |
| Spain | 2 | 1% |
| France | 1 | <1% |
| Brazil | 1 | <1% |
| Sweden | 1 | <1% |
| India | 1 | <1% |
| Italy | 1 | <1% |
| Other | 4 | 3% |
| Unknown | 125 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 40 | 27% |
| Student > Ph. D. Student | 34 | 23% |
| Professor > Associate Professor | 13 | 9% |
| Student > Master | 13 | 9% |
| Student > Postgraduate | 9 | 6% |
| Other | 24 | 16% |
| Unknown | 16 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 61 | 41% |
| Biochemistry, Genetics and Molecular Biology | 19 | 13% |
| Mathematics | 12 | 8% |
| Computer Science | 10 | 7% |
| Medicine and Dentistry | 8 | 5% |
| Other | 18 | 12% |
| Unknown | 21 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 15 May 2014.
All research outputs
#7,443,958
of 22,755,127 outputs
Outputs from BMC Bioinformatics
#3,021
of 7,271 outputs
Outputs of similar age
#34,185
of 95,189 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 47 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,271 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 gotten more attention than average, scoring higher than 50% 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 95,189 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.