A novel algorithm for time-varying gene regulatory networks identification with biological state change detection

This paper proposes a dynamic nonlinear autoregressive model based algorithm for gene regulatory networks (GRNs) identification with biological stage change detection using the L1-regularization. This allows subtle variations in the same state to be penalized and prominent changes across adjacent states to be captured. Furthermore, by assuming local-stationarity within each detected biological state, the number of network parameters can be significantly reduced. Simulation results using a dynamic synthetic dataset and a real time course Drosophila Melanogaster DNA microarray dataset shows that the proposed method is able to achieve better identification accuracy in comparing with other conventional approaches. Moreover, it is able to identify the biological state change point precisely and identify the GRNs with effectiveness. These suggest that the proposed approach may provide an attractive alternative in GRNs identification problem.