| Title |
Adaptive filtering of microarray gene expression data based on Gaussian mixture decomposition
|
|---|---|
| Published in |
BMC Bioinformatics, March 2013
|
| DOI | 10.1186/1471-2105-14-101 |
| Pubmed ID | |
| Authors |
Michal Marczyk, Roman Jaksik, Andrzej Polanski, Joanna Polanska |
| Abstract |
DNA microarrays are used for discovery of genes expressed differentially between various biological conditions. In microarray experiments the number of analyzed samples is often much lower than the number of genes (probe sets) which leads to many false discoveries. Multiple testing correction methods control the number of false discoveries but decrease the sensitivity of discovering differentially expressed genes. Concerning this problem, filtering methods for improving the power of detection of differentially expressed genes were proposed in earlier papers. These techniques are two-step procedures, where in the first step some pool of non-informative genes is removed and in the second step only the pool of the retained genes is used for searching for differentially expressed genes. |
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 % |
|---|---|---|
| India | 1 | 25% |
| United States | 1 | 25% |
| Norway | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Netherlands | 1 | 2% |
| Unknown | 52 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 30% |
| Researcher | 8 | 15% |
| Student > Master | 5 | 9% |
| Professor > Associate Professor | 4 | 7% |
| Student > Bachelor | 4 | 7% |
| Other | 12 | 22% |
| Unknown | 5 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 39% |
| Biochemistry, Genetics and Molecular Biology | 7 | 13% |
| Computer Science | 7 | 13% |
| Engineering | 6 | 11% |
| Mathematics | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 6 | 11% |
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 31 January 2018.
All research outputs
#6,015,157
of 22,701,287 outputs
Outputs from BMC Bioinformatics
#2,249
of 7,254 outputs
Outputs of similar age
#50,267
of 197,462 outputs
Outputs of similar age from BMC Bioinformatics
#44
of 146 outputs
Altmetric has tracked 22,701,287 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 7,254 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 68% 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 197,462 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.