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

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advanced biclustering applications in R
Amazon Cloud
Applying Biclustering Methods
Biclustering Algorithm
biclustering analysis
biclustering applications for cloud computing
Biclustering Methods
Biclustering Using R
Big Data Analysis
Category=PBT
Category=PS
Checkerboard Structure
Cloud Computing
computational biology
Data Matrix
Diffuse Large B Cell Lymphoma
DLBCL
ensemble methods
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
factor analysis for biclustering acquisition
flexible overlapped biclustering
Fractional Polynomial Model
Gene Expression Profiling
identity by descent detection
iterative signature algorithm
local patterns in a big data matrix
market segmentation techniques
Membership Vector
MFA
miRNA integration
molecular modeling
multivariate data analysis
NBA Data
NPC1
plaid algorithm
Plaid Model
R package BiclustGUI
Rest Package
Row Column Combinations
Shiny Apps
Target Prediction
Target Prediction Algorithm
TCGA
TCGA Data
TCGA Dataset
Yeast Data

Product details

  • ISBN 9781482208238
  • Weight: 884g
  • Dimensions: 156 x 234mm
  • Publication Date: 04 Oct 2016
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.
About

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.