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Mendeley readers
Attention Score in Context
| Title |
An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation)
|
|---|---|
| Published in |
BMC Bioinformatics, December 2009
|
| DOI | 10.1186/1471-2105-10-409 |
| Pubmed ID | |
| Authors |
Martin J Aryee, José A Gutiérrez-Pabello, Igor Kramnik, Tapabrata Maiti, John Quackenbush |
| Abstract |
Microarray gene expression time-course experiments provide the opportunity to observe the evolution of transcriptional programs that cells use to respond to internal and external stimuli. Most commonly used methods for identifying differentially expressed genes treat each time point as independent and ignore important correlations, including those within samples and between sampling times. Therefore they do not make full use of the information intrinsic to the data, leading to a loss of power. |
Mendeley readers
The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 3% |
| Germany | 3 | 2% |
| Korea, Republic of | 1 | <1% |
| Sweden | 1 | <1% |
| United Kingdom | 1 | <1% |
| South Africa | 1 | <1% |
| Japan | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 145 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 47 | 30% |
| Student > Ph. D. Student | 40 | 25% |
| Professor > Associate Professor | 14 | 9% |
| Student > Master | 13 | 8% |
| Student > Bachelor | 8 | 5% |
| Other | 25 | 16% |
| Unknown | 12 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 75 | 47% |
| Computer Science | 18 | 11% |
| Biochemistry, Genetics and Molecular Biology | 16 | 10% |
| Mathematics | 11 | 7% |
| Medicine and Dentistry | 9 | 6% |
| Other | 15 | 9% |
| Unknown | 15 | 9% |
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 08 January 2013.
All research outputs
#20,178,031
of 22,691,736 outputs
Outputs from BMC Bioinformatics
#6,828
of 7,255 outputs
Outputs of similar age
#157,898
of 164,969 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 58 outputs
Altmetric has tracked 22,691,736 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 7,255 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 164,969 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 58 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.