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A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen

Overview of attention for article published in BMC Systems Biology, November 2012
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  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
3 tweeters

Citations

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9 Dimensions

Readers on

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38 Mendeley
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Title
A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen
Published in
BMC Systems Biology, November 2012
DOI 10.1186/1752-0509-6-138
Pubmed ID
Authors

Galina Lebedeva, Azusa Yamaguchi, Simon P Langdon, Kenneth Macleod, David J Harrison, Lebedeva G, Yamaguchi A, Langdon SP, Macleod K, Harrison DJ

Abstract

Estrogen receptors alpha (ER) are implicated in many types of female cancers, and are the common target for anti-cancer therapy using selective estrogen receptor modulators (SERMs, such as tamoxifen). However, cell-type specific and patient-to-patient variability in response to SERMs (from suppression to stimulation of cancer growth), as well as frequent emergence of drug resistance, represents a serious problem. The molecular processes behind mixed effects of SERMs remain poorly understood, and this strongly motivates application of systems approaches. In this work, we aimed to establish a mathematical model of ER-dependent gene expression to explore potential mechanisms underlying the variable actions of SERMs.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 3%
Germany 1 3%
Unknown 36 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 34%
Student > Ph. D. Student 11 29%
Student > Bachelor 3 8%
Other 2 5%
Professor > Associate Professor 2 5%
Other 3 8%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 45%
Biochemistry, Genetics and Molecular Biology 7 18%
Psychology 2 5%
Immunology and Microbiology 1 3%
Computer Science 1 3%
Other 6 16%
Unknown 4 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 2012.
All research outputs
#10,786,307
of 17,958,727 outputs
Outputs from BMC Systems Biology
#507
of 1,117 outputs
Outputs of similar age
#87,575
of 162,753 outputs
Outputs of similar age from BMC Systems Biology
#8
of 21 outputs
Altmetric has tracked 17,958,727 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,117 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 49th percentile – i.e., 49% 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 162,753 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.