Cover -- Half-title -- Title -- Copyright -- Dedication -- Contents -- Contributors -- Editor -- Foreword -- Contributors -- Foreword -- Introduction -- 1 Technique of microarrays: microarray platforms -- Introduction -- Microarray platforms -- cDNA microarrays -- Oligonucleotide microarrays -- Novel microarray platforms -- Target-labeling -- Microarray image analysis -- Accuracy and reliability of microarray platforms -- Outlook -- References -- 2 Quantitative quality control of microarray experiments: toward accurate gene expression measurements -- Introduction -- Accurate information acquisition from microarrays -- Quantitative data filtering and normalization, advantages of the quality score approach -- Quality measures from cyanine images -- Quality measures from the TD image -- R-Q LOWESS normalization and its efficiency -- Qf-weighted mean and t-test -- The accuracy of gene expression measurements by microarrays -- Discussion and conclusions -- Acknowledgments -- References -- 3 Statistical analysis of gene expression data -- Abstract -- Introduction -- Statistical background and challenges with microarray data -- Data processing -- Data acquisition and computation expression indices -- Spotted arrays -- Affymetrix arrays -- MAS5 -- dChip -- RMA -- Summary -- Quality control (QC) -- dChip outliers -- Present/absent percentage -- Brightness/dimness -- 5'3' ratio -- Gene filtering: identifying differentially expressed genes -- Observational filtering metrics -- Present/absent calls -- Fold change -- Absolute difference -- Statistical filtering metrics -- Confidence interval for the fold-change -- Classical statistical testing -- Correction for multiple testing/false discovery rate -- Significance analysis of microarrays (SAM) -- Clustering/data visualization: unsupervised learning -- Hierarchical clustering -- Self-organizing maps (SOM).

Principal components (PCA) -- Additional clustering techniques -- Classification/prediction: supervised learning -- Prediction models -- Logistic regression -- Classification and regression trees (CART) -- Random forests -- K-nearest neighbor (KNN) -- Linear discriminant analysis (LDA) -- Support vector machines (SVMs) -- Neural networks -- Summary of prediction methods -- Model assessment and validation -- Experimental design -- Spotted array design -- Sample allocation -- Sample size -- References -- 4 Genomic stratification in patients with heart failure -- Introduction -- Animal models of HF -- Hypertrophy and HF -- Akt and apoptosis -- Tumor necrosis factor-Alpha (TNF-Alpha) -- Alpha1b-adrenergic receptor (Alpha1b-AR) -- Atrial natriuretic peptide (ANP) ablation -- Development, progression and rescue of HF in animal models -- Myocardial infarction -- Pressure overload -- Pharmacologic induction of heart failure -- Animal models of cardiac conductance abnormality -- A mouse heart gene expression database -- Cardiac development and the fetal gene program of HF -- Human HF -- Left ventricular assist devices in human HF -- Final common pathway? -- Peripheral blood mononuclear cell profiling in HF -- Conclusions -- References -- 5 Gene expression profiling for the diagnosis of acute leukemias -- Introduction -- Characterization of acute myeloid leukemia by microarray analyses -- Class prediction in AML -- Distinction of acute myeloid leukemia from acute lymphoblastic leukemia based on gene expression profiles -- Class prediction of cytomorphologically defined subgroups of AML -- Identification of specific genetic abnormalities based on gene expression profiles -- AML with genetic aberrations detectable on the molecular level can be identified, based on their gene expression profiles -- Approaching class discovery in AML.

Comparison of protein expression and gene expression levels as assessed by flow cytometry and microarrays -- Prognostic studies in AML based on genetic profiling -- Characterization of acute lymphoblastic leukemia -- Specific gene expression patterns are associated with distinct subtypes of B-precursor and T-precursor ALL -- Prediction of response in ALL -- A global approach for the diagnosis in leukemia using gene expression profiling -- Conclusions and future directions -- References -- 6 Gene expression profiling can distinguish tumor subclasses of breast carcinomas -- Introduction -- Gene expression profiles associated with breast cancer phenotypes -- Sporadic breast cancer -- Hereditary breast cancer -- Correlation between transcriptional profiles and histological types -- Detecting metastatic potential and tumor aggressiveness -- Prediction of clinical outcome -- Prognosis -- Response to therapy -- Tamoxifen -- Taxanes -- Integration of data from other genomic technologies -- Microarrays in clinical practice -- The future -- References -- 7 Gene expression profiling in lymphoid malignancies -- Introduction -- B-cell chronic lymphocytic leukemia (B-CLL) -- Gene expression signatures of B-CLL cells in response to DNA damage -- Mantle cell lymphoma (MCL) -- Follicular lymphoma (FL) -- Diffuse large B-cell lymphoma (DLBCL) -- Conclusions -- Acknowledgments -- References -- 8 mRNA profiling of pancreatic beta-cells: investigating mechanisms of diabetes -- The pancreatic beta-cell in health and disease -- Microarray analysis of pancreatic islets and primary clonal beta-cells -- A handful of experimental challenges -- Systematic false positives: the balanced choice of an experimental system -- Systematic false negatives: a metaphysical challenge -- Finding the molecular Nemo in an ocean of data.

Avoiding random false positives by linking independent data sets -- Exploiting the probe redundancy on Affymetrix expression arrays -- Conclusions and perspectives -- Acknowledgments -- References -- 9 Prediction of response and resistance to treatment by gene expression profiling -- Introduction -- The biodiversity of diffuse large B-cell lymphoma -- Pediatric and adult acute lymphoblastic leukemia (ALL): models for prediction of relapse and response to treatment -- Breast cancer - enhancing chances for cure -- From gene expression analysis to treatment plan: common predictors of response in heterogenous cancers -- Conclusions -- References -- Index.