Inferring gene regulatory networks with nonlinear models via exploiting sparsity

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper considers the problem of inferring gene regulatory networks using time series data. A nonlinear model is assumed for the gene expression profiles, whereas the microarray data follows a linear Gaussian model. A particle filter based approach is proposed to estimate the gene expression profiles and the parameters are estimated online using Kalman filter. In order to capture the inherent sparsity of the gene networks, LASSO based least square optimization is performed. The performance of the proposed algorithm is compared with the extended Kalman filter (EKF) algorithm using Mean Square Error (MSE) as the fidelity criterion. The simulations are performed using the synthetic as well as real data and the proposed algorithm is observed to outperform the EKF in the scenarios considered.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages725-728
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period25/3/1230/3/12

Fingerprint

Genes
Extended Kalman filters
Gene expression
Microarrays
Kalman filters
Mean square error
Time series

Keywords

  • Gene regulatory network
  • Kalman filter
  • LASSO
  • parameter estimation
  • particle filter

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Noor, A., Serpedin, E., Nounou, M., & Nounou, H. (2012). Inferring gene regulatory networks with nonlinear models via exploiting sparsity. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 725-728). [6287986] https://doi.org/10.1109/ICASSP.2012.6287986

Inferring gene regulatory networks with nonlinear models via exploiting sparsity. / Noor, Amina; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem.

2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 725-728 6287986.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Noor, A, Serpedin, E, Nounou, M & Nounou, H 2012, Inferring gene regulatory networks with nonlinear models via exploiting sparsity. in 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings., 6287986, pp. 725-728, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, Kyoto, Japan, 25/3/12. https://doi.org/10.1109/ICASSP.2012.6287986
Noor A, Serpedin E, Nounou M, Nounou H. Inferring gene regulatory networks with nonlinear models via exploiting sparsity. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 725-728. 6287986 https://doi.org/10.1109/ICASSP.2012.6287986
Noor, Amina ; Serpedin, Erchin ; Nounou, Mohamed ; Nounou, Hazem. / Inferring gene regulatory networks with nonlinear models via exploiting sparsity. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. pp. 725-728
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