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Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

Overview of attention for article published in BMC Systems Biology, November 2013
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Title
Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network
Published in
BMC Systems Biology, November 2013
DOI 10.1186/1752-0509-7-126
Pubmed ID
Authors

Ashish Misra, Ganesh Sriram

Abstract

Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types.

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

Geographical breakdown

Country Count As %
Japan 2 3%
United States 2 3%
Germany 2 3%
Unknown 53 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 24%
Researcher 10 17%
Professor 7 12%
Student > Master 7 12%
Student > Postgraduate 5 8%
Other 10 17%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 54%
Biochemistry, Genetics and Molecular Biology 8 14%
Computer Science 6 10%
Engineering 2 3%
Chemical Engineering 1 2%
Other 0 0%
Unknown 10 17%
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 22 November 2013.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from BMC Systems Biology
#651
of 1,132 outputs
Outputs of similar age
#140,238
of 224,523 outputs
Outputs of similar age from BMC Systems Biology
#31
of 58 outputs
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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 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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