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An extended gene protein/products boolean network model including post-transcriptional regulation

Overview of attention for article published in Theoretical Biology and Medical Modelling, May 2014
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1 tweeter

Citations

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9 Dimensions

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19 Mendeley
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Title
An extended gene protein/products boolean network model including post-transcriptional regulation
Published in
Theoretical Biology and Medical Modelling, May 2014
DOI 10.1186/1742-4682-11-s1-s5
Pubmed ID
Authors

Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Alessandro Vasciaveo

Abstract

Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 5%
Luxembourg 1 5%
Unknown 17 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 32%
Student > Ph. D. Student 3 16%
Student > Bachelor 3 16%
Other 1 5%
Student > Doctoral Student 1 5%
Other 2 11%
Unknown 3 16%
Readers by discipline Count As %
Computer Science 4 21%
Biochemistry, Genetics and Molecular Biology 3 16%
Agricultural and Biological Sciences 3 16%
Medicine and Dentistry 2 11%
Chemistry 2 11%
Other 1 5%
Unknown 4 21%

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 01 August 2014.
All research outputs
#3,278,595
of 4,095,760 outputs
Outputs from Theoretical Biology and Medical Modelling
#111
of 151 outputs
Outputs of similar age
#81,566
of 102,292 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#10
of 13 outputs
Altmetric has tracked 4,095,760 research outputs across all sources so far. This one is in the 2nd percentile – i.e., 2% of other outputs scored the same or lower than it.
So far Altmetric has tracked 151 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 1st percentile – i.e., 1% 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 102,292 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.