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Visualizing chemical structure-subcellular localization relationships using fluorescent small molecules as probes of cellular transport

Overview of attention for article published in Journal of Cheminformatics, October 2013
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3 X users
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1 Facebook page

Citations

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37 Mendeley
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Title
Visualizing chemical structure-subcellular localization relationships using fluorescent small molecules as probes of cellular transport
Published in
Journal of Cheminformatics, October 2013
DOI 10.1186/1758-2946-5-44
Pubmed ID
Authors

Gus R Rosania, Kerby Shedden, Nan Zheng, Xinyuan Zhang

Abstract

To study the chemical determinants of small molecule transport inside cells, it is crucial to visualize relationships between the chemical structure of small molecules and their associated subcellular distribution patterns. For this purpose, we experimented with cells incubated with a synthetic combinatorial library of fluorescent, membrane-permeant small molecule chemical agents. With an automated high content screening instrument, the intracellular distribution patterns of these chemical agents were microscopically captured in image data sets, and analyzed off-line with machine vision and cheminformatics algorithms. Nevertheless, it remained challenging to interpret correlations linking the structure and properties of chemical agents to their subcellular localization patterns in large numbers of cells, captured across large number of images.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Netherlands 1 3%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 22%
Professor > Associate Professor 5 14%
Student > Ph. D. Student 5 14%
Student > Master 5 14%
Student > Bachelor 4 11%
Other 2 5%
Unknown 8 22%
Readers by discipline Count As %
Chemistry 8 22%
Agricultural and Biological Sciences 5 14%
Biochemistry, Genetics and Molecular Biology 4 11%
Computer Science 3 8%
Engineering 2 5%
Other 7 19%
Unknown 8 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 October 2013.
All research outputs
#13,160,609
of 22,725,280 outputs
Outputs from Journal of Cheminformatics
#638
of 828 outputs
Outputs of similar age
#107,378
of 207,956 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 7 outputs
Altmetric has tracked 22,725,280 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 21st percentile – i.e., 21% 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 207,956 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.