Methods of Microarray Data Analysis

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Shoemaker, Jennifer S. (Επιμελητής έκδοσης), Lin, Simon M. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2005.
• Έκδοση: IV.
• Θέματα: Medicine.; Cancer research.; Human genetics.; Biomedicine.; Human Genetics.; Cancer Research.

As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA conference plays a role in this evolving field by providing a forum in which investors can analyze the same data sets using different methods. METHODS OF MICROARRAY DAT...

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• 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.