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Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay

Overview of attention for article published in BMC Bioinformatics, October 2013
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Title
Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay
Published in
BMC Bioinformatics, October 2013
DOI 10.1186/1471-2105-14-308
Pubmed ID
Authors

Gregoire Pau, Thomas Walter, Beate Neumann, Jean-Karim Hériché, Jan Ellenberg, Wolfgang Huber

Abstract

The combination of time-lapse imaging of live cells with high-throughput perturbation assays is a powerful tool for genetics and cell biology. The Mitocheck project employed this technique to associate thousands of genes with transient biological phenotypes in cell division, cell death and migration. The original analysis of these data proceeded by assigning nuclear morphologies to cells at each time-point using automated image classification, followed by description of population frequencies and temporal distribution of cellular states through event-order maps. One of the choices made by that analysis was not to rely on temporal tracking of the individual cells, due to the relatively low image sampling frequency, and to focus on effects that could be discerned from population-level behaviour.

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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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Luxembourg 1 2%
Unknown 43 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 25%
Researcher 10 23%
Student > Master 6 14%
Student > Doctoral Student 4 9%
Student > Bachelor 3 7%
Other 8 18%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 41%
Biochemistry, Genetics and Molecular Biology 10 23%
Engineering 4 9%
Computer Science 3 7%
Physics and Astronomy 2 5%
Other 5 11%
Unknown 2 5%
Attention Score in Context

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 17 October 2013.
All research outputs
#16,099,609
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#5,488
of 7,454 outputs
Outputs of similar age
#132,479
of 213,596 outputs
Outputs of similar age from BMC Bioinformatics
#71
of 107 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 18th percentile – i.e., 18% 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 213,596 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.