Methods of Microarray Data Analysis IV (eBook)

Cancer: Clinical Challenges and Opportunities.- Gene Expression Data and Survival Analysis.- The Needed Replicates of Arrays in Microarray Experiments for Reliable Statistical Evaluation.- Pooling Information Across Different Studies and Oligonucleotide Chip Types to Identify Prognostic Genes for Lung Cancer.- Application of Survival and Meta-analysis to Gene Expression Data Combined from Two Studies.- Making Sense of Human Lung Carcinomas Gene Expression Data: Integration and Analysis of Two Affymetrix Platform Experiments.- Entropy and Survival-based Weights to Combine Affymetrix Array Types and Analyze Differential Expression and Survival.- Associating Microarray Data with a Survival Endpoint.- Differential Correlation Detects Complex Associations Between Gene Expression and Clinical Outcomes in Lung Adenocarcinomas.- Probabilistic Lung Cancer Models Conditioned on Gene Expression Microarray Data.- Integration of Microarray Data for a Comparative Study of Classifiers and Identification of Marker Genes.- Use of Micro Array Data via Model-based Classification in the Study and Prediction of Survival from Lung Cancer.- Microarray Data Analysis of Survival Times of Patients with Lung Adenocarcinomas Using ADC and K-Medians Clustering.- Higher Dimensional Approach for Classification of Lung Cancer Microarray Data.- Microarray Data Analysis Using Neural Network Classifiers and Gene Selection Methods.- A Combinatorial Approach to the Analysis of Differential Gene Expression Data.- Genes Associated with Prognosis in Adenocarcinoma Across Studies at Multiple Institutions.