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

Publication Type:
Journal Article
Citation:
Eurasip Journal on Bioinformatics and Systems Biology, 2013, 2013 (1)
Issue Date:
2013-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
1687-4153-2013-13.pdfPublished Version1.01 MB
Adobe PDF
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.
Please use this identifier to cite or link to this item: