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Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network

Overview of attention for article published in BMC Bioinformatics, June 2012
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1 X user

Citations

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47 Dimensions

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90 Mendeley
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Title
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
Published in
BMC Bioinformatics, June 2012
DOI 10.1186/1471-2105-13-131
Pubmed ID
Authors

Nguyen Xuan Vinh, Madhu Chetty, Ross Coppel, Pramod P Wangikar

Abstract

Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks.

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X Demographics

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

Geographical breakdown

Country Count As %
Brazil 3 3%
Colombia 1 1%
India 1 1%
United Kingdom 1 1%
United States 1 1%
Poland 1 1%
Unknown 82 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 30%
Researcher 11 12%
Student > Master 8 9%
Professor 8 9%
Student > Postgraduate 7 8%
Other 23 26%
Unknown 6 7%
Readers by discipline Count As %
Computer Science 24 27%
Agricultural and Biological Sciences 23 26%
Biochemistry, Genetics and Molecular Biology 12 13%
Mathematics 8 9%
Engineering 7 8%
Other 7 8%
Unknown 9 10%
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 19 June 2012.
All research outputs
#15,245,883
of 22,668,244 outputs
Outputs from BMC Bioinformatics
#5,360
of 7,247 outputs
Outputs of similar age
#106,703
of 167,239 outputs
Outputs of similar age from BMC Bioinformatics
#78
of 111 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 167,239 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.