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The Local Edge Machine: inference of dynamic models of gene regulation

Overview of attention for article published in Genome Biology, October 2016
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6 X users

Citations

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61 Mendeley
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Title
The Local Edge Machine: inference of dynamic models of gene regulation
Published in
Genome Biology, October 2016
DOI 10.1186/s13059-016-1076-z
Pubmed ID
Authors

Kevin A. McGoff, Xin Guo, Anastasia Deckard, Christina M. Kelliher, Adam R. Leman, Lauren J. Francey, John B. Hogenesch, Steven B. Haase, John L. Harer

Abstract

We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Canada 1 2%
Unknown 58 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 31%
Student > Ph. D. Student 15 25%
Student > Doctoral Student 4 7%
Student > Master 4 7%
Student > Bachelor 3 5%
Other 10 16%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 38%
Biochemistry, Genetics and Molecular Biology 18 30%
Computer Science 5 8%
Mathematics 4 7%
Physics and Astronomy 2 3%
Other 3 5%
Unknown 6 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 04 July 2019.
All research outputs
#14,388,865
of 25,374,917 outputs
Outputs from Genome Biology
#3,817
of 4,467 outputs
Outputs of similar age
#165,315
of 323,147 outputs
Outputs of similar age from Genome Biology
#54
of 65 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
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 13th percentile – i.e., 13% 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 323,147 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.