An effective parameter estimation approach for the inference of gene networks

Abstract

Constructing genetic regulatory networks from expression profiles is one of the most important issues in systems biology research. To automate the procedure of network construction, this work presents an integrated approach for network inference, in which the parameter identification and parameter optimization techniques are developed to deal with the scalability and network robustness problems, respectively. To validate the proposed approach, experiments have been conducted on several artificial and real datasets. The results show that our approach can be used to infer robust gene networks with desired system behaviors successfully. © Springer-Verlag Berlin Heidelberg 2013.