1. What Statisticians Should Know About Microarray Gene Expression Technology
Stephen Welle
2. Where Statistics and Molecular Microarray Experiments Biology Meet
Diana M. Kelmansky
3. Multiple Hypothesis Testing: A Methodological Overview
Anthony Almudevar
4. Gene Selection with the d-sequence MethodXing Qiu and Lev B Klebanov
5. Using of Normalizations for Gene Expression Analysis Peter Bubelíny
6. Constructing Multivariate Prognostic Gene Signatures with Censored Survival Data
Derick R. Peterson
7. Clustering of Gene-Expression Data via Normal Mixture Models
G.J. McLachlan, L.K. Flack, S.K. Ng, and K. Wang
8. Network-based Analysis of Multivariate Gene Expression Data
Wei Zhi, Jane Minturn, Eric Rappaport, Garrett Brodeur, and Hongzhe Li
9. Genomic Outlier Detection in High-throughput Data Analysis
Debashis Ghosh
10. Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiment
Andreas Scherer, Manhong Dai, and Fan Meng
11. Aggregation Effect in Microarray Data Analysis
Linlin Chen, Anthony Almudevar and Lev Klebanov
12. Test for Normality of the Gene Expression Data
Bobosharif Shokirov
