A new approach to dynamic fuzzy modeling of genetic regulatory networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number5613189
Pages (from-to)263-272
Journal / PublicationIEEE Transactions on Nanobioscience
Volume9
Issue number4
Publication statusPublished - Dec 2010

Abstract

In this paper, the dynamic fuzzy modeling approach is applied for modeling genetic regulatory networks from gene expression data. The parameters of the dynamic fuzzy model and the optimal number of fuzzy rules for the fuzzy gene network can be obtained via the proposed modeling approach from the measured gene expression data. One of the main features of the proposed approach is that the prior qualitative knowledge on the network structure can be easily incorporated in the proposed identification algorithm, so that the faster learning convergence of the algorithm can be achieved. Two sets of data, one the synthetic data, and the other the experimental SOS DNA repair network data with structural knowledge, have been used to validate the proposed modeling approach. It is shown that the proposed approach is effective in modeling genetic regulatory networks. © 2006 IEEE.

Research Area(s)

  • Fuzzy clusters, fuzzy modeling, gene expression data, genetic regulatory networks, SOS DNA repair networks