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Inference of gene regulatory networks by extended Kalman filtering using gene expression time seriesdata

Fouladi, R ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. Publisher: 2012
  3. Abstract:
  4. In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model's parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions' true values. Through the extended Kalman filtering process, these coefficients are updated in such a way that the predicted gene expressions follow the ones observed. Finally, connections between each two genes are inferred based on these coefficients
  5. Keywords:
  6. Extended Kalman filtering ; Gene expression ; Gene regulatory network modelling ; Gene regulatory networks ; Gene-gene interaction ; Kalman-filtering ; Linear terms ; Nonlinear stochastic model ; Nonlinear terms ; State vector ; Time-series gene expression data ; Algorithms ; Extended Kalman filters ; Mathematical models ; Random processes ; Bioinformatics
  7. Source: BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms ; 2012 , Pages 150-155 ; 9789898425904 (ISBN)
  8. URL: http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=2pLybSY20cs=&t=1