Methods of Microarray Data Analysis

By: Jennifer S. Shoemaker, Simon M. Lin (Editor)

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Hardcover | 29 October 2004

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276 Pages

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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 Data Analysis IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on classification (Volume I), pattern recognition (Volume II), and quality control issues (Volume III).

In this volume, four lung cancer data sets are the focus of analysis. We highlight three tutorial papers, including one to assist with a basic understanding of lung cancer, a review of survival analysis in the gene expression literature, and a paper on replication. In addition, 14 papers presented at the conference are included. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of the art of microarray data analysis.

Jennifer Shoemaker is a faculty member in the Department of Biostatistics and Bioinformatics and the Director of the Bioinformatics Unit for the Cancer and Leukemia Group B Statistical Center, Duke University Medical Center. Simon Lin is a faculty member in the Department of Biostatistics and Bioinformatics and the Manager of the Duke Bioinformatics Shared Resource, Duke University Medical Center.

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You Can Find This Book In

Non-Fiction Science Biology, Life Sciences Biochemistry Medicine Clinical & Internal Medicine Diseases & Disorders Oncology Life Sciences in General Genetics excluding Medical DNA & Genome Medicine in General

Public Health & Preventive Medicine Epidemiology & Medical Statistics Pre-Clinical Medicine & Basic Sciences Medical Genetics

Introduction
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 of Data
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 Indentification of Marker Genes
Use of Microarray 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
Index
Table of Contents provided by Publisher. All Rights Reserved.

Methods of Microarray Data Analysis

At a Glance

Hardcover

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