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Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

Overview of attention for article published in Biological Research, November 2014
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  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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Title
Neutral space analysis for a Boolean network model of the fission yeast cell cycle network
Published in
Biological Research, November 2014
DOI 10.1186/0717-6287-47-64
Pubmed ID
Authors

Gonzalo A Ruz, Tania Timmermann, Javiera Barrera, Eric Goles

Abstract

Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.

X Demographics

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

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 20%
Student > Ph. D. Student 2 20%
Student > Doctoral Student 1 10%
Student > Master 1 10%
Professor > Associate Professor 1 10%
Other 1 10%
Unknown 2 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 50%
Computer Science 2 20%
Engineering 1 10%
Unknown 2 20%
Attention Score in Context

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 08 December 2017.
All research outputs
#16,045,990
of 25,371,288 outputs
Outputs from Biological Research
#253
of 642 outputs
Outputs of similar age
#206,059
of 369,530 outputs
Outputs of similar age from Biological Research
#4
of 11 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 642 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 56% 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 369,530 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 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.