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Bayesian methods for expression-based integration of various types of genomics data
- Source :
- EURASIP Journal on Bioinformatics and Systems Biology
- Publication Year :
- 2013
- Publisher :
- Springer Science and Business Media LLC, 2013.
-
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.
- Subjects :
- Computer science
Systems biology
Bayesian probability
Genomics
Computational biology
computer.software_genre
Bayesian inference
General Biochemistry, Genetics and Molecular Biology
Statistical power
03 medical and health sciences
0302 clinical medicine
Hierarchical models
medicine
Shrinkage priors
030304 developmental biology
0303 health sciences
Research
Integrative analysis
Integrated approach
medicine.disease
Bayesian modeling
Expression (mathematics)
Computer Science Applications
Computational Mathematics
030220 oncology & carcinogenesis
Data mining
computer
Glioblastoma
Subjects
Details
- ISSN :
- 16874153
- Volume :
- 2013
- Database :
- OpenAIRE
- Journal :
- EURASIP Journal on Bioinformatics and Systems Biology
- Accession number :
- edsair.doi.dedup.....b5580f32c8a4a398995d5c0044bef785