Genetic Networks and Soft Computing

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ISSN: 1545-5963

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	Sushmita Mitra, Ranajit Das, Yoichi Hayashi, "Genetic Networks and Soft Computing," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 99, no. 1, pp. , , 5555.

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	@article{ 10.1109/TCBB.2009.39, author = {Sushmita Mitra and Ranajit Das and Yoichi Hayashi}, title = {Genetic Networks and Soft Computing}, journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {99}, number = {1}, issn = {1545-5963}, year = {5555}, doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.39}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

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	TY - JOUR JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics TI - Genetic Networks and Soft Computing IS - 1 SN - 1545-5963 SP EP EPD - A1 - Sushmita Mitra, A1 - Ranajit Das, A1 - Yoichi Hayashi, PY - 5555 KW - Gene Regulatory Networks KW - Reverse Engineering KW - gene expression KW - Neural nets KW - Genetic Algorithm KW - Fuzzy set KW - Models KW - Pattern Recognition KW - Artificial Intelligence KW - Machine learning KW - Learning KW - Artificial Intelligence KW - Computing Methodologies KW - microarray VL - 99 JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics ER -

Sushmita Mitra, Indian Statistical Institute, Kolkata

Ranajit Das, Indian Statistical Institute, Kolkata

Yoichi Hayashi, Meiji University, Kawasaki

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.39

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, and towards prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks like 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 like fuzzy sets, evolutionary strategies and neurocomputing have been found to help in providing low cost, acceptable solutions in the presence of various types of uncertainties. In this article we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.

Index Terms:

Gene Regulatory Networks, Reverse Engineering, gene expression, Neural nets, Genetic Algorithm, Fuzzy set, Models, Pattern Recognition, Artificial Intelligence, Machine learning, Learning, Artificial Intelligence, Computing Methodologies, microarray

Citation:

Sushmita Mitra, Ranajit Das, Yoichi Hayashi, "Genetic Networks and Soft Computing," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 27 Apr. 2009. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.39>

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