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Discrimination of cell cycle phases in PCNA-immunolabeled cells

Overview of attention for article published in BMC Bioinformatics, May 2015
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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4 X users
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3 Wikipedia pages

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177 Mendeley
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Title
Discrimination of cell cycle phases in PCNA-immunolabeled cells
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0618-9
Pubmed ID
Authors

Felix Schönenberger, Anja Deutzmann, Elisa Ferrando-May, Dorit Merhof

Abstract

Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable and frequently used markers of cell cycle progression. Proliferating cell nuclear antigen (PCNA) is a protein which is involved in DNA replication and has cell cycle dependent properties. In this work, we present a tool to identify cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution. Single time point images of PCNA-immunolabeled cells are acquired using confocal and widefield fluorescence microscopy. In order to discriminate different cell cycle phases, an optimized processing pipeline is proposed. For this purpose, we provide an in-depth analysis and selection of appropriate features for classification, an in-depth evaluation of different classification algorithms, as well as a comparative analysis of classification performance achieved with confocal versus widefield microscopy images. We show that the proposed processing chain is capable of automatically classifying cell cycle phases in PCNA-immunolabeled cells from single time point images, independently of the technique of image acquisition. Comparison of confocal and widefield images showed that for the proposed approach, the overall classification accuracy is slightly higher for confocal microscopy images. Overall, automated identification of cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution, is feasible for both confocal and widefield images.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
Unknown 175 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 23%
Student > Master 28 16%
Student > Bachelor 27 15%
Researcher 18 10%
Student > Doctoral Student 10 6%
Other 20 11%
Unknown 34 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 62 35%
Agricultural and Biological Sciences 33 19%
Medicine and Dentistry 13 7%
Computer Science 6 3%
Engineering 6 3%
Other 21 12%
Unknown 36 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 22 March 2024.
All research outputs
#5,895,972
of 22,851,489 outputs
Outputs from BMC Bioinformatics
#2,171
of 7,292 outputs
Outputs of similar age
#68,379
of 265,860 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 129 outputs
Altmetric has tracked 22,851,489 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,292 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 gotten more attention than average, scoring higher than 69% 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 265,860 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 129 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 63% of its contemporaries.