Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data

Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data

Editor(s): Frank Emmert-Streib, Matthias Dehmer

Published Online: 8 APR 2013 05:04AM EST

Print ISBN: 9783527332625

Online ISBN: 9783527665471

DOI: 10.1002/9783527665471

Series Editor(s): M. Dehmer, Frank Emmert-Streib

About this Book

This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.

Table of contents

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  1. Part One: General Overview

    1. Chapter 2

      Overview of Public Cancer Databases, Resources, and Visualization Tools (pages 27–40)

      Frank Emmert-Streib, Ricardo de Matos Simoes, Shailesh Tripathi and Matthias Dehmer

  2. Part Two: Bayesian Methods

  3. Part Three: Network-Based Approaches

    1. Chapter 11

      Discriminant and Network Analysis to Study Origin of Cancer (pages 193–214)

      Li Chen, Ye Tian, Guoqiang Yu, David J. Miller, Ie-Ming Shih and Yue Wang

  4. Part Four: Phenotype Influence of DNA Copy Number Aberrations

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