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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer
|
|---|---|
| Published in |
BMC Systems Biology, May 2011
|
| DOI | 10.1186/1752-0509-5-67 |
| Pubmed ID | |
| Authors |
Changyu Shen, Yiwen Huang, Yunlong Liu, Guohua Wang, Yuming Zhao, Zhiping Wang, Mingxiang Teng, Yadong Wang, David A Flockhart, Todd C Skaar, Pearlly Yan, Kenneth P Nephew, Tim HM Huang, Lang Li |
| Abstract |
Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood. |
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 States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Chile | 1 | 3% |
| United States | 1 | 3% |
| France | 1 | 3% |
| Unknown | 29 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 24% |
| Student > Ph. D. Student | 6 | 18% |
| Professor > Associate Professor | 4 | 12% |
| Professor | 3 | 9% |
| Student > Postgraduate | 2 | 6% |
| Other | 7 | 21% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 39% |
| Biochemistry, Genetics and Molecular Biology | 7 | 21% |
| Medicine and Dentistry | 3 | 9% |
| Computer Science | 3 | 9% |
| Engineering | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 3 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 29 October 2020.
All research outputs
#4,146,915
of 22,675,759 outputs
Outputs from BMC Systems Biology
#127
of 1,142 outputs
Outputs of similar age
#20,956
of 109,834 outputs
Outputs of similar age from BMC Systems Biology
#2
of 19 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 88% 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 109,834 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 79% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.