Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks feedback linearization component. Thus, RODES automatically discovers the structure, estimate the parameter, and identify the molecular mechanisms, even when information is missing from the data. It produces systems of ordinary differential equations from experimental or simulated microarray time series data. On simulated data the accuracy and the CPU time were very good. This is due to reducing the reversing of an ordinary differential equations system to that of individual algebraic equations, and to the-possibility of incorporating common a priori knowledge. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming and neural networks, applicable to large gene networks.
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