Reverse engineering sparse gene regulatory networks using cubature Kalman filter and compressed sensing

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.

Original languageEnglish
Article number205763
JournalAdvances in Bioinformatics
Volume2013
DOIs
Publication statusPublished - 2013

Fingerprint

Compressed sensing
Reverse engineering
Gene Regulatory Networks
Kalman filters
Genes
Gene expression
Space Simulation
Gene Expression
Benchmarking
Markov Chains
Nonlinear Dynamics
Computer Simulation
Markov processes
Scalability
Linear Models
Datasets

ASJC Scopus subject areas

  • Computer Science Applications
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Biomedical Engineering

Cite this

@article{5f0b6ef0ea9c4a4d862467dd5fa62dcd,
title = "Reverse engineering sparse gene regulatory networks using cubature Kalman filter and compressed sensing",
abstract = "This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cram{\'e}r-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.",
author = "Amina Noor and Erchin Serpedin and Mohamed Nounou and Hazem Nounou",
year = "2013",
doi = "10.1155/2013/205763",
language = "English",
volume = "2013",
journal = "Advances in Bioinformatics",
issn = "1687-8027",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - Reverse engineering sparse gene regulatory networks using cubature Kalman filter and compressed sensing

AU - Noor, Amina

AU - Serpedin, Erchin

AU - Nounou, Mohamed

AU - Nounou, Hazem

PY - 2013

Y1 - 2013

N2 - This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.

AB - This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.

UR - http://www.scopus.com/inward/record.url?scp=84878744128&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84878744128&partnerID=8YFLogxK

U2 - 10.1155/2013/205763

DO - 10.1155/2013/205763

M3 - Article

C2 - 23737768

AN - SCOPUS:84878744128

VL - 2013

JO - Advances in Bioinformatics

JF - Advances in Bioinformatics

SN - 1687-8027

M1 - 205763

ER -