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Analysis of feedback mechanisms with unknown delay using sparse multivariate autoregressive method

Ip, EH and Zhang, Q and Sowinski, T and Simpson, SL (2015) Analysis of feedback mechanisms with unknown delay using sparse multivariate autoregressive method. PLoS ONE, 10 (8).

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© 2015 Ip et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This paper discusses the study of two interacting processes in which a feedback mechanism exists between the processes. The study was motivated by problems such as the circadian oscillation of gene expression where two interacting protein transcriptions form both negative and positive feedback loops with long delays to equilibrium. Traditionally, data of this type could be examined using autoregressive analysis. However, in circadian oscillation the order of an autoregressive model cannot be determined a priori. We propose a sparse multivariate autoregressive method that incorporates mixed linear effects into regression analysis, and uses a forward-backward greedy search algorithm to select nonzero entries in the regression coefficients, the number of which is constrained not to exceed a pre-specified number. A small simulation study provides preliminary evidence of the validity of the method. Besides the circadian oscillation example, an additional example of blood pressure variations using data from an intervention study is used to illustrate the method and the interpretation of the results obtained from the sparse matrix method. These applications demonstrate how sparse representation can be used for handling high dimensional variables that feature dynamic, reciprocal relationships.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Ip, EH
Zhang, Q
Sowinski, T
Simpson, SL
ContributionContributors NameEmailPitt UsernameORCID
Date: 7 August 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS ONE
Volume: 10
Number: 8
DOI or Unique Handle: 10.1371/journal.pone.0131371
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 30 Jun 2016 19:42
Last Modified: 02 Feb 2019 16:58


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