Reverse-engineering of quantitative, dynamic generegulatory network (GRN) models from time-series gene expression data is becoming important as such data are increasingly generated for research and other purposes. A key problem in the reverse-engineering process is the underdetermined nature of these data. Because of this, the reverseengineered GRN models often lack robustness and perform poorly when used to simulate system responses to new conditions. In this study, we present a novel method capable of inferring robust GRN models from multi-condition GRN experiments. This study uses two important computational intelligence methods: artificial neural networks and particle swarm optimization.
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