Inferring gene regulatory networks with nonlinear models via exploiting sparsity Conference Paper uri icon

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. © 2012 IEEE.

name of conference

  • ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing

published proceedings

  • 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

author list (cited authors)

  • Noor, A., Serpedin, E., Nounou, M., & Nounou, H

citation count

  • 2

complete list of authors

  • Noor, Amina||Serpedin, Erchin||Nounou, Mohamed||Nounou, Hazem

publication date

  • March 2012

publisher