modelling of time series microarray data using dynamic bayesian network

BioMed Central Page 1 of 1 (page number not for citation purposes) Retrovirology Open Access Poster presentation Modelling of time series microarray data using dynamic Bayesian network KG Srinivasa*, S Seema and Manoj Jaiswal Address: Department of computer science and engineering, M.S. Ramaiah institute of technology, Bangalore, India * Corresponding author Gene Regulatory Network represents how the genes inter- act with each other. Using genetic network modelling, it is possible to explain the cell functions at molecular level. DNA microarrays can measure the expression levels of thousands of genes simultaneously. Two steps method adapted to model large-scale Gene Regulatory Networks using time series microarray data. Firstly, genes are clus- tered based on existing biological knowledge (Gene Ontology annotations) and then a dynamic Bayesian network applied in order to model causal relationships between genes in each cluster. Finally the learned sub-networks are integrated to make a global network. This project aims at inferring the regulatory network that pro- vides us the interaction between the various genes. Our aim is to apply data mining technique to gene expression data and infer regulatory network for various experiments, which include experiments in good and bad conditions using the information available in Gene Ontology. from Frontiers of Retrovirology: Complex retroviruses, retroelements and their hosts Montpellier, France. 21-23 September 2009 Published: 24 September 2009 Retrovirology 2009, 6(Suppl 2):P84 doi:10.1186/1742-4690-6-S2-P84 <supplement> <title> <p>Frontiers of Retrovirology: Complex retroviruses, retroelements and their hosts</p> </title> <note>Meeting abstracts - A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1742-4690-6-S2-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1742-4690-6-S2-info.pdf</url> </supplement> This abstract is available from: http://www.retrovirology.com/content/6/S2/P84 © 2009 Srinivasa et al; licensee BioMed Central Ltd.

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Page 1: Modelling of time series microarray data using dynamic Bayesian network

BioMed Central

Page 1 of 1(page number not for citation purposes)

Retrovirology

Open AccessPoster presentationModelling of time series microarray data using dynamic Bayesian networkKG Srinivasa*, S Seema and Manoj Jaiswal

Address: Department of computer science and engineering, M.S. Ramaiah institute of technology, Bangalore, India

* Corresponding author

Gene Regulatory Network represents how the genes inter-act with each other. Using genetic network modelling, it ispossible to explain the cell functions at molecular level.DNA microarrays can measure the expression levels ofthousands of genes simultaneously. Two steps methodadapted to model large-scale Gene Regulatory Networksusing time series microarray data. Firstly, genes are clus-tered based on existing biological knowledge (GeneOntology annotations) and then a dynamic Bayesian net-work applied in order to model causal relationshipsbetween genes in each cluster. Finally the learned sub-net-works are integrated to make a global network. Thisproject aims at inferring the regulatory network that pro-vides us the interaction between the various genes. Ouraim is to apply data mining technique to gene expressiondata and infer regulatory network for various experiments,which include experiments in good and bad conditionsusing the information available in Gene Ontology.

from Frontiers of Retrovirology: Complex retroviruses, retroelements and their hostsMontpellier, France. 21-23 September 2009

Published: 24 September 2009

Retrovirology 2009, 6(Suppl 2):P84 doi:10.1186/1742-4690-6-S2-P84

<supplement> <title> <p>Frontiers of Retrovirology: Complex retroviruses, retroelements and their hosts</p> </title> <note>Meeting abstracts - A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1742-4690-6-S2-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1742-4690-6-S2-info.pdf</url> </supplement>

This abstract is available from: http://www.retrovirology.com/content/6/S2/P84

http://www.retrovirology.com/content/6/S2/P84

http://www.biomedcentral.com/

http://www.biomedcentral.com/info/about/charter/

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