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Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene

Bin Jia1, Xiaodong Wang2

  • 1Intelligent Fusion Technology, Germantown, Inc., MD 20876, USA.

EURASIP Journal on Bioinformatics & Systems Biology
|April 9, 2014
PubMed
Summary
This summary is machine-generated.

A new regularized expectation-maximization (rEM) algorithm enhances parameter estimation in dynamic systems by incorporating sparsity. This method is effective for gene regulatory network inference, improving accuracy with real and synthetic data.

Keywords:
Expectation-maximizationForward-backward recursionGaussian approximationGene regulatory networkNonlinear dynamic systemParameter estimationSparsity

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Machine Learning

Background:

  • Parameter estimation is crucial for dynamic systems, particularly in systems biology.
  • The Expectation-Maximization (EM) algorithm is widely used but cannot leverage sparsity.
  • Gene regulatory network inference often involves sparse parameter structures.

Purpose of the Study:

  • To propose a novel regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems.
  • To integrate sparse prior information using Maximum a Posteriori (MAP) estimation.
  • To apply the rEM algorithm to the problem of gene regulatory network inference.

Main Methods:

  • Developed a regularized expectation-maximization (rEM) algorithm.
  • The expectation step utilizes forward and backward Gaussian approximation filtering and smoothing.
  • The maximization step employs a re-weighted iterative thresholding method.

Main Results:

  • The proposed rEM algorithm effectively incorporates sparse priors into parameter estimation.
  • Demonstrated the algorithm's applicability to gene regulatory network inference.
  • Validation using both synthetic and real biological data confirmed the algorithm's effectiveness.

Conclusions:

  • The rEM algorithm provides an effective solution for sparse parameter estimation in nonlinear dynamic systems.
  • This approach significantly advances gene regulatory network inference capabilities.
  • The method shows promise for applications requiring sparse parameter identification in complex systems.