Issue No. 01 - January-February (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.39
Sushmita Mitra , Indian Statistical Institute, Kolkata
Ranajit Das , Indian Statistical Institute, Kolkata
Yoichi Hayashi , Meiji University, Kawasaki
The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.
Gene regulatory networks, reverse engineering, gene expression, microarray, artificial neural networks, genetic algorithms, fuzzy sets.
Y. Hayashi, R. Das and S. Mitra, "Genetic Networks and Soft Computing," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 94-107, 2009.