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A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development

Overview of attention for article published in BMC Systems Biology, June 2012
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Title
A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development
Published in
BMC Systems Biology, June 2012
DOI 10.1186/1752-0509-6-77
Pubmed ID
Authors

Brandilyn Stigler, Helen M Chamberlin

Abstract

Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments.

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The data shown below were collected from the profile of 1 X user 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 28%
Researcher 7 19%
Professor > Associate Professor 4 11%
Student > Master 4 11%
Student > Bachelor 3 8%
Other 5 14%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 33%
Biochemistry, Genetics and Molecular Biology 9 25%
Computer Science 4 11%
Physics and Astronomy 2 6%
Chemistry 2 6%
Other 3 8%
Unknown 4 11%
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 03 July 2012.
All research outputs
#20,656,820
of 25,374,917 outputs
Outputs from BMC Systems Biology
#827
of 1,132 outputs
Outputs of similar age
#138,597
of 177,442 outputs
Outputs of similar age from BMC Systems Biology
#38
of 47 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.