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