Applied Biclustering Methods for Big and High-Dimensional Data Using R (eBook, PDF)

Redaktion: Kasim, Adetayo; Talloen, Willem; Hochreiter, Sepp; Kaiser, Sebastian; Shkedy, Ziv

• Format: PDF

Produktbeschreibung

This book shows how to apply biclustering methods to find local patterns in a big data matrix. It 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. 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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 433
• Erscheinungstermin: 3. Oktober 2016
• Englisch
• ISBN-13: 9781482208245
• Artikelnr.: 46758437

Autorenporträt

Adetayo Kasim is a senior research statistician at Durham University. Ziv Shkedy is a professor in the Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat) in the Center for Statistics at the University of Hasselt. Sebastian Kaiser is a professor in the Department of Statistics in the Faculty of Mathematics, Informatics and Statistics at Ludwig-Maximilians University of Munich. Sepp Hochreiter is a professor and head of the Institute of Bioinformatics at Johannes Kepler University Linz. Willem Talloen is a principal statistician at the Janssen Pharmaceutical Companies of Johnson & Johnson.

Inhaltsangabe

Introduction. From Cluster Analysis to Biclustering. Biclustering Methods:
¿-biclustering and FLOC Algorithm. The xMotif Algorithm. The Bimax
Algorithm. The Plaid Model. Spectral Biclustering. FABIA. Iterative
Signature Algorithm. Ensemble Methods and Robust Solutions. Case Studies
and Applications: Gene Expression Experiments in Drug Discovery.
Biclustering Methods in Chemoinformatics and Molecular Modeling.
Integrative Analysis of miRNA and mRNA Data. Enrichment of Gene Expression
Modules using Multiple Factor Analysis and Biclustering. Ranking of
Biclusters in Drug Discovery Experiments. HapFABIA: Biclustering for
Detecting Identity by Descent. Overcoming Data Dimensionality Problems in
Market Segmentation. Identification of Local Patterns in the NBA
Performance Indicators. R Tools for Biclustering: The BiclustGUI Package.
We R a Community: Including a New Package in BiclustGUI. Biclustering for
Cloud Computing. The biclustGUI Shiny App. Bibliography. Index.