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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
|
|---|---|
| Published in |
BMC Bioinformatics, August 2006
|
| DOI | 10.1186/1471-2105-7-391 |
| Pubmed ID | |
| Authors |
Sébastien Lemieux |
| Abstract |
The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. |
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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Germany | 1 | 6% |
| Unknown | 15 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 53% |
| Student > Ph. D. Student | 4 | 24% |
| Student > Postgraduate | 2 | 12% |
| Professor > Associate Professor | 1 | 6% |
| Lecturer > Senior Lecturer | 1 | 6% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 59% |
| Computer Science | 2 | 12% |
| Arts and Humanities | 1 | 6% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Physics and Astronomy | 1 | 6% |
| Other | 2 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 July 2012.
All research outputs
#20,160,460
of 22,669,724 outputs
Outputs from BMC Bioinformatics
#6,820
of 7,247 outputs
Outputs of similar age
#64,409
of 66,480 outputs
Outputs of similar age from BMC Bioinformatics
#37
of 38 outputs
Altmetric has tracked 22,669,724 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% 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 66,480 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.