| Title |
Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity
|
|---|---|
| Published in |
Algorithms for Molecular Biology, April 2009
|
| DOI | 10.1186/1748-7188-4-7 |
| Pubmed ID | |
| Authors |
Koji Kadota, Yuji Nakai, Kentaro Shimizu |
| Abstract |
To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility. |
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 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 3% |
| Japan | 3 | 3% |
| Germany | 2 | 2% |
| Canada | 2 | 2% |
| United States | 2 | 2% |
| Russia | 1 | <1% |
| Netherlands | 1 | <1% |
| Unknown | 90 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 39 | 38% |
| Student > Ph. D. Student | 23 | 22% |
| Professor > Associate Professor | 9 | 9% |
| Student > Postgraduate | 6 | 6% |
| Student > Master | 6 | 6% |
| Other | 12 | 12% |
| Unknown | 9 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 50 | 48% |
| Computer Science | 14 | 13% |
| Medicine and Dentistry | 11 | 11% |
| Engineering | 3 | 3% |
| Physics and Astronomy | 2 | 2% |
| Other | 13 | 13% |
| Unknown | 11 | 11% |
Attention Score in Context
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#20,160,460
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Outputs from Algorithms for Molecular Biology
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Outputs of similar age from Algorithms for Molecular Biology
#2
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