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Methods of Microarray Data Analysis IV

  • Book
  • © 2005

Overview

Editors:
  1. Jennifer S. Shoemaker

    1. Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Simon M. Lin

    1. Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham, USA

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    You can also search for this editor in PubMed   Google Scholar

  • In a single reference, the reader can learn about the most up-to-date methods on associating microarray data with a survival endpoint

  • All papers included in the publication analyze the same data sets

  • Thus, the audience should benefit from the critical evaluation of various techniques

  • A reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses

  • Includes supplementary material: sn.pub/extras

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Introduction

    Pages 1-8

  3. Cancer: Clinical Challenges and Opportunities

    • David G. Beer
    Pages 9-20

  4. Gene Expression Data and Survival Analysis

    • Peter J. Park
    Pages 21-34

  5. The Needed Replicates of Arrays in Microarray Experiments for Reliable Statistical Evaluation

    • Sue-Jane Wang, Chen James J.
    Pages 35-49

  6. Pooling Information Across Different Studies and Oligonucleotide Chip Types to Identify Prognostic Genes for Lung Cancer

    • Jeffrey S. Morris, Guosheng Yin, Keith Baggerly, Chunlei Wu, Li Zhang
    Pages 51-66

  7. Application of Survival and Meta-analysis to Gene Expression Data Combined from Two Studies

    • Linda Warnock, Richard Stephens, JoAnn Coleman
    Pages 67-80

  8. Making Sense of Human Lung Carcinomas Gene Expression Data: Integration and Analysis of Two Affymetrix Platform Experiments

    • Xiwu Lin, Daniel Park, Sergio Eslava, Kwan R. Lee, Raymond L.H. Lam, Lei A. Zhu
    Pages 81-94

  9. Entropy and Survival-based Weights to Combine Affymetrix Array Types and Analyze Differential Expression and Survival

    • Jianhua Hu, Guosheng Yin, Jeffrey S. Morris, Li Zhang, Fred A. Wright
    Pages 95-108

  10. Associating Microarray Data with a Survival Endpoint

    • Sin-Ho Jung, Kouros Owzar, Stephen George
    Pages 109-120

  11. Differential Correlation Detects Complex Associations Between Gene Expression and Clinical Outcomes in Lung Adenocarcinomas

    • Kerby Shedden, Jeremy Taylor
    Pages 121-131

  12. Probabilistic Lung Cancer Models Conditioned on Gene Expression Microarray Data

    • Craig Friedman, Wenbo Cao, Cheng Fan
    Pages 133-146

  13. Integration of Microarray Data for a Comparative Study of Classifiers and Identification of Marker Genes

    • Daniel Berrar, Brian Sturgeon, Ian Bradbury, C. Stephen Downes, Werner Dubitzky
    Pages 147-162

  14. Use of Micro Array Data via Model-based Classification in the Study and Prediction of Survival from Lung Cancer

    • Liat Ben-Tovim Jones, Shu-Kay Ng, Christophe Ambroise, Katrina Monico, Nazim Khan, Geoff McLachlan
    Pages 163-173

  15. Microarray Data Analysis of Survival Times of Patients with Lung Adenocarcinomas Using ADC and K-Medians Clustering

    • Wenting Zhou, Weichen Wu, Nathan Palmer, Emily Mower, Noah Daniels, Lenore Cowen et al.
    Pages 175-190

  16. Higher Dimensional Approach for Classification of Lung Cancer Microarray Data

    • F. Crimins, R. Dimitri, T. Klein, N. Palmer, L. Cowen
    Pages 191-205

  17. Microarray Data Analysis Using Neural Network Classifiers and Gene Selection Methods

    • Gaolin Zheng, E. Olusegun George, Giri Narasimhan
    Pages 207-222

  18. A Combinatorial Approach to the Analysis of Differential Gene Expression Data

    • Michael A. Langston, Lan Lin, Xinxia Peng, Nicole E. Baldwin, Christopher T. Symons, Bing Zhang et al.
    Pages 223-238

  19. Genes Associated with Prognosis in Adenocarcinoma Across Studies at Multiple Institutions

    • Andrew V. Kossenkov, Ghislain Bidaut, Michael F. Ochs
    Pages 239-253

  20. Back Matter

    Pages 255-256
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Keywords

About this book

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.

Editors and Affiliations

  • Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham, USA

    Jennifer S. Shoemaker, Simon M. Lin

Bibliographic Information

  • Book Title: Methods of Microarray Data Analysis IV

  • Editors: Jennifer S. Shoemaker, Simon M. Lin

  • DOI: https://doi.org/10.1007/b100565

  • Publisher: Springer New York, NY

  • eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)

  • Copyright Information: Springer-Verlag US 2005

  • Hardcover ISBN: 978-0-387-23074-0Published: 29 October 2004

  • Softcover ISBN: 978-1-4899-8784-6Published: 29 November 2014

  • eBook ISBN: 978-0-387-23077-1Published: 16 January 2006

  • Edition Number: 1

  • Number of Pages: XVI, 256

  • Topics: Human Genetics, Cancer Research

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