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Mendeley readers
Attention Score in Context
| Title |
Empirical bayes analysis of sequencing-based transcriptional profiling without replicates
|
|---|---|
| Published in |
BMC Bioinformatics, November 2010
|
| DOI | 10.1186/1471-2105-11-564 |
| Pubmed ID | |
| Authors |
Zhijin Wu, Bethany D Jenkins, Tatiana A Rynearson, Sonya T Dyhrman, Mak A Saito, Melissa Mercier, LeAnn P Whitney |
| Abstract |
Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. |
Mendeley readers
The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 5% |
| Germany | 4 | 2% |
| Japan | 2 | 1% |
| United Kingdom | 2 | 1% |
| Switzerland | 1 | <1% |
| Sweden | 1 | <1% |
| Canada | 1 | <1% |
| France | 1 | <1% |
| Mexico | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 143 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 47 | 28% |
| Researcher | 43 | 26% |
| Student > Master | 20 | 12% |
| Professor > Associate Professor | 13 | 8% |
| Student > Doctoral Student | 10 | 6% |
| Other | 22 | 13% |
| Unknown | 13 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 97 | 58% |
| Biochemistry, Genetics and Molecular Biology | 14 | 8% |
| Mathematics | 6 | 4% |
| Environmental Science | 6 | 4% |
| Earth and Planetary Sciences | 4 | 2% |
| Other | 24 | 14% |
| Unknown | 17 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 11 November 2015.
All research outputs
#3,706,253
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#1,418
of 7,234 outputs
Outputs of similar age
#14,711
of 87,660 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 63 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,234 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 done well, scoring higher than 80% 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 87,660 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.