Bayesian models for gene expression with DNA microarray data

Journal of the American Statistical Association

Volumn 97, Issue 457, 2002, Pages 88-99

  Ibrahim, J G ^a   Chen, M H ^b   Gray, R J ^a  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Bayesian inference; DNA; Gene expression; Hierarchical prior; L measure; Microarray

Indexed keywords

EID: 0036489048     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/016214502753479257     Document Type: Article

References (18)

1. Ratio-based decisions and the quantitative analysis of CDNA microarray images
2. Cluster analysis and display of genome-wide expression patterns
3. Model choice: A minimum posterior predictive loss approach
4. Adaptive rejection sampling for gibbs sampling
5. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
6. Criterion based method; For bayesian model assessment
7. A predictive approach to the analysis of designed experiments
8. Predictive model selection
9. On differential variability of expression ratios improving statistical inference about gene expression changes from microarray data
10. Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces, cerevisiae by microarray hybridization
11. Interpreting Patterns of gene expression with self-organizing maps: Methods and application m hematopoietic differentiation
12. On Assessing prior distributions and bayesian regresstun analysis with G-prior distributions