NDLI: Genetic Networks and Soft Computing

Content Provider	IEEE Xplore Digital Library
Author	Mitra, S. Das, R. Hayashi, Y.
Copyright Year	2004
Abstract	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.
Sponsorship	IEEE Computer Society
Page Count	14
File Size	2985887
Starting Page	94
Ending Page	107
File Format	PDF
ISSN	15455963
Volume Number	8
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetics Computer networks Cellular networks Drugs Information analysis Biochemistry Fluids and secretions Data mining Gene expression Reverse engineering fuzzy sets. Gene regulatory networks reverse engineering gene expression microarray artificial neural networks genetic algorithms
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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Genetic Networks and Soft Computing

Bioinformatics with soft computing

Discovering Gene Networks with a Neural-Genetic Hybrid

Discovering gene networks with a neural-genetic hybrid

Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

Bioinformatics with soft computing

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series

Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing

A method for modelling genetic regulatory networks by using evolving connectionist systems and microarray gene expression data

Genetic Networks and Soft Computing

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Genetic Networks and Soft Computing

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Discovering Gene Networks with a Neural-Genetic Hybrid

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