Stephen W. Looney
Springer Science & Business Media, 2002 - Science - 214 pages
Leading biostatisticians and biomedical researchers describe many of the key techniques used to solve commonly occurring data analytic problems in molecular biology, and demonstrate how these methods can be used in the development of new markers for exposure to a risk factor or for disease outcomes. Major areas of application include microarray analysis, proteomic studies, image quantitation, genetic susceptibility and association, evaluation of new biomarkers, and power analysis and sample size.
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Affymetrix agreement algorithm allele analytic application approach assay assess associated biomarker Biometrics Biostatistical breast cancer cell chapter Clin clinical trials closed testing clustering coefficients controls correlation covariance data analysis data set described determination developed dichotomous disease distribution doxorubicin edited effect electrophoresis estimate evaluation exact example experiment exposure gene expression genetic genotype glutathione S-transferase groups GSTM1 Herceptin image quantitation interaction latent class model linear marker matrix measure Methods and Protocols microarray instances molecular biology molecules multiple testing multiresolution level negative null hypothesis obtained oligo outcome overexpression p-values paradigm parameters patients performed permutation phenotype polymorphisms power and sample principal components probe PROC MULTTEST procedure prognostic factor programs proteins proteomics random regression risk S-Plus sample size sample size calculations sequence significant specific specimens spots statistical methods statistical power studies Subheading Table tion treatment tumor two-dimensional validity variability variance wavelet