Contents -- Preface -- Introduction of Authors -- Chapter 1 DNA Microarray Technology -- 1.1. Experimental Procedure -- 1.2. Experimental Design -- 1.3. Quality Control -- 1.4. Interpretation of DNA Microarray Data -- 1.5. Advantages and Disadvantages -- Chapter 2 Applications of DNA Microarray Technology in Cancer Research -- 2.1 Solid Tumors -- 2.2 Blood Cancers -- 2.3. Our DNA Microarray Study Using Mouse Model of BCR-ABL-Induced Leukemia -- 2.3.1. Leukemia mouse model study -- 2.3.1.1. Rationale -- 2.3.1.2. Experimental design -- 2.3.1.3. Results -- 2.3.2. Cell line study -- Chapter 3 Current Analytical Methods of DNA Microarray Data -- 3.1. Experimental Design -- 3.2. Method -- 3.2.1. Robust multi-chip averaging (RMA) -- 3.2.2. iterPLIER -- 3.3 Quality Control Diagnostics -- 3.3.1. Saturation -- 3.3.1.1. Raw intensities for arrays containing a saturated probe -- 3.3.2. Transformed intensities across arrays -- 3.3.3. Normalized intensities across arrays -- 3.3.4. Scatterplot of normalized intensities -- 3.3.5. Average MA plot of normalized intensities -- 3.4. Statistical Analysis -- 3.4.1. Analysis of variance (ANOVA) model -- 3.4.2. Contrasts -- 3.4.2.1. Fs permutation p-value distribution -- 3.4.2.2. p- and q-value summary -- 3.4.2.3. Volcano plot -- 3.5. Coefficient of Variation Analysis -- Chapter 4 A Novel Method for DNA Microarray Data Analysis: SDL Global Optimization Method -- 4.1. Research Subjects -- 4.2. Experimental Design -- 4.2.1. Rationale -- 4.3. Fold Change Analysis -- 4.4. More Information on SDL Global Optimization -- 4.4.1. Genetic algorithms (GAs) -- 4.4.2. SDL global optimization algorithms -- Chapter 5 Applications of the SDL Global Optimization Method in DNA Microarray Data Analysis -- 5.1. Leukemia Cell Line Study -- 5.1.1. Introduction -- 5.1.2. Datasets -- 5.1.3. Analysis strategies.

5.1.3.1. Data sorting based on 20 chromosome groups (Fig. 5.1) -- 5.1.3.2. Dynamics of gene expression -- 5.1.3.3. Absolute difference between P190 and P210 (Fig. 5.3) -- 5.1.3.4. Prefiltering process (gene selection) -- 5.1.3.5. Grouping genes based on the deviations -- 5.1.3.6. Analyzing the selected 8832 genes -- 5.1.3.7. The second sharp jump of deviations -- 5.1.3.8. Top 13 genes which are most differentially expressed across P190 and P210 -- 5.1.4. Discussion and conclusion -- 5.2. Analyses of Publicly Available Human Microarray Data -- 5.2.1. Introduction -- 5.2.2. Datasets -- 5.2.2.1. Colon data -- 5.2.2.2. Leukemia data -- 5.3. Overall Methodology -- 5.3.1. Orthogonal arrays (OAs) and sampling procedure -- 5.3.2. Objective function -- 5.3.3. Search space reduction for global search -- 5.3.4. Mathematical form of SDL optimization -- 5.3.4.1. Definition of local minima -- 5.3.4.2. Definition of global minima -- 5.3.4.3. How to find the global minima -- 5.3.5. Multi-subset class predictor -- 5.3.6. Validation (predicting through a voting mechanism) -- 5.4. Experimental Results -- 5.5. Discussion -- 5.6. Conclusion -- Chapter 6 General Discussion and Future Directions -- References -- Index.