Title :
A method for modelling genetic regulatory networks by using evolving connectionist systems and microarray gene expression data
Author :
Kasabov, Nikola K. ; Dimitrov, Dimiter S.
Author_Institution :
Knowledge Eng. & Discovery Res. Inst., Auckland Univ. of Technol., New Zealand
Abstract :
The paper describes the problem of discovering genetic networks from time course gene expression data (the reverse engineering approach) and introduces a novel method for using evolving connectionist systems (ECOS) for this task. A case study is used to illustrate the approach. Genetic regulatory networks, once constructed, can be potentially used to model the behaviour of a cell or an organism from initial conditions.
Keywords :
fuzzy neural nets; genetic algorithms; molecular biophysics; neurophysiology; physiological models; Zadeh Mamdani fuzzy rules; evolving connectionist systems; fuzzy neural network; genetic regulatory networks; microarray gene expression data; molecular biology; Biological system modeling; Cancer; Cells (biology); Fuzzy neural networks; Gene expression; Genetics; Knowledge engineering; Neural networks; Paper technology; Reverse engineering;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1198127