Bayesian methods for expression-based integration of various types of genomics data Computational methods for biomarker discovery and systems biology research

Jennings, EM; Morris, JS; Carroll, RJ; Manyam, GC; Baladandayuthapani, V

Bayesian methods for expression-based integration of various types of genomics data Computational methods for biomarker discovery and systems biology research

Jennings, EM Morris, JS Carroll, RJ Manyam, GC Baladandayuthapani, V

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Publication Type:: Journal Article
Citation:: Eurasip Journal on Bioinformatics and Systems Biology, 2013, 2013 (1)
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Jennings, EM	en_US
dc.contributor.author	Morris, JS	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.contributor.author	Manyam, GC	en_US
dc.contributor.author	Baladandayuthapani, V	en_US
dc.date.available	2013-09-06	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Eurasip Journal on Bioinformatics and Systems Biology, 2013, 2013 (1)	en_US
dc.identifier.issn	1687-4145	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118060
dc.description.abstract	We propose methods to integrate data across several genomic platforms using a hierarchical Bayesian analysis framework that incorporates the biological relationships among the platforms to identify genes whose expression is related to clinical outcomes in cancer. This integrated approach combines information across all platforms, leading to increased statistical power in finding these predictive genes, and further provides mechanistic information about the manner in which the gene affects the outcome. We demonstrate the advantages of the shrinkage estimation used by this approach through a simulation, and finally, we apply our method to a Glioblastoma Multiforme dataset and identify several genes potentially associated with the patients' survival. We find 12 positive prognostic markers associated with nine genes and 13 negative prognostic markers associated with nine genes. © 2013 Jennings et al.; licensee Springer.	en_US
dc.relation.ispartof	Eurasip Journal on Bioinformatics and Systems Biology	en_US
dc.relation.isbasedon	10.1186/1687-4153-2013-13	en_US
dc.subject.classification	Bioinformatics	en_US
dc.title	Bayesian methods for expression-based integration of various types of genomics data Computational methods for biomarker discovery and systems biology research	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	2013	en_US
utslib.for	0104 Statistics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	2013	en_US

Abstract:

We propose methods to integrate data across several genomic platforms using a hierarchical Bayesian analysis framework that incorporates the biological relationships among the platforms to identify genes whose expression is related to clinical outcomes in cancer. This integrated approach combines information across all platforms, leading to increased statistical power in finding these predictive genes, and further provides mechanistic information about the manner in which the gene affects the outcome. We demonstrate the advantages of the shrinkage estimation used by this approach through a simulation, and finally, we apply our method to a Glioblastoma Multiforme dataset and identify several genes potentially associated with the patients' survival. We find 12 positive prognostic markers associated with nine genes and 13 negative prognostic markers associated with nine genes. © 2013 Jennings et al.; licensee Springer.

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http://hdl.handle.net/10453/118060