Applied Biclustering Methods for Big and High-Dimensional Data Using R

Adetayo Kasim, Ziv Shkedy, Sebastian Kaiser, Sepp Hochreiter, Willem Talloen

October 4, 2016 by Chapman and Hall/CRC
Reference - 407 Pages - 117 B/W Illustrations
ISBN 9781482208238 - CAT# K21579
Series: Chapman & Hall/CRC Biostatistics Series


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  • Presents novel methods and algorithms for biclustering analysis, such as the plaid algorithm, factor analysis for biclustering acquisition (FABIA), iterative signature algorithm (ISA), flexible overlapped biclustering (FLOC) algorithm, and ensemble methods for diagnostics
  • Introduces the new R package BiclustGUI
  • Explains how to implement the methods using several versatile R packages
  • Explores applications in drug development, biology, medicine, marketing, sports, and genetics
  • Develops biclustering applications for cloud computing, enabling users to replicate the analyses online without having to install R
  • Provides examples, R packages, and more on a supplementary website

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Proven Methods for Big Data Analysis

As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix.

The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.