Discovering relationships in genetic regulatory networks

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dc.contributorDatta, Aniruddha
dc.creatorPal, Ranadip
dc.date.accessioned2004-11-15T19:51:01Z
dc.date.accessioned2017-04-07T19:49:08Z
dc.date.available2004-11-15T19:51:01Z
dc.date.available2017-04-07T19:49:08Z
dc.date.created2004-08
dc.date.issued2004-11-15
dc.description.abstractThe development of cDNA microarray technology has made it possible to simultaneously monitor the expression status of thousands of genes. A natural use for this vast amount of information would be to try and figure out inter-gene relationships by studying the gene expression patterns across different experimental conditions and to build Gene Regulatory Networks from these data. In this thesis, we study some of the issues involved in Genetic Regulatory Networks. One of them is to discover and elucidate multivariate logical predictive relations among gene expressions and to demonstrate how these logical relations based on coarse quantization closely reflect corresponding relations in the continuous data. The other issue involves construction of synthetic Probabilistic Boolean Networks with particular attractor structures. These synthetic networks help in testing of various algorithms like Bayesian Connectivity based approach for design of Probabilistic Boolean Networks.
dc.identifier.urihttp://hdl.handle.net/1969.1/1230
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectGenetic Regulatory Networks
dc.subjectMultivariate Relationships
dc.subjectBoolean Networks
dc.titleDiscovering relationships in genetic regulatory networks
dc.typeBook
dc.typeThesis

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