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Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, February 2014
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Title
Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother
Published in
EURASIP Journal on Bioinformatics & Systems Biology, February 2014
DOI 10.1186/1687-4153-2014-3
Pubmed ID
Authors

Jehandad Khan, Nidhal Bouaynaya, Hassan M Fathallah-Shaykh

Abstract

: It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 29%
Student > Master 3 13%
Researcher 3 13%
Student > Bachelor 3 13%
Professor 1 4%
Other 3 13%
Unknown 4 17%
Readers by discipline Count As %
Engineering 8 33%
Computer Science 5 21%
Biochemistry, Genetics and Molecular Biology 2 8%
Business, Management and Accounting 1 4%
Mathematics 1 4%
Other 3 13%
Unknown 4 17%

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 13 February 2014.
All research outputs
#11,076,455
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#40
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Outputs of similar age
#160,478
of 191,977 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 1 outputs
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