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Full Description
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.
Contents
Introduction. From Cluster Analysis to Biclustering. Biclustering Methods:δ-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.
