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The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

Overview of attention for article published in Genome Biology, May 2006
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
4 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
470 Dimensions

Readers on

mendeley
491 Mendeley
citeulike
19 CiteULike
connotea
2 Connotea
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Title
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
Published in
Genome Biology, May 2006
DOI 10.1186/gb-2006-7-5-r36
Pubmed ID
Authors

Richard Bonneau, David J Reiss, Paul Shannon, Marc Facciotti, Leroy Hood, Nitin S Baliga, Vesteinn Thorsson

Abstract

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 491 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 28 6%
United Kingdom 8 2%
Belgium 3 <1%
Sweden 3 <1%
Brazil 3 <1%
Switzerland 2 <1%
Japan 2 <1%
Mexico 2 <1%
Italy 1 <1%
Other 10 2%
Unknown 429 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 143 29%
Researcher 113 23%
Student > Master 43 9%
Professor > Associate Professor 38 8%
Student > Bachelor 33 7%
Other 75 15%
Unknown 46 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 208 42%
Biochemistry, Genetics and Molecular Biology 80 16%
Computer Science 68 14%
Engineering 22 4%
Physics and Astronomy 14 3%
Other 49 10%
Unknown 50 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 19 May 2020.
All research outputs
#5,405,755
of 25,374,647 outputs
Outputs from Genome Biology
#2,909
of 4,467 outputs
Outputs of similar age
#14,809
of 83,745 outputs
Outputs of similar age from Genome Biology
#7
of 19 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 34th percentile – i.e., 34% 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 83,745 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 19 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.