Integrative cluster analysis in bioinformatics
Author(s)
Bibliographic Information
Integrative cluster analysis in bioinformatics
Wiley, 2015
- : cloth
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery.
This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications.
Key Features:
Offers a contemporary review of clustering methods and applications in the field of bioinformatics, with particular emphasis on gene expression analysis
Provides an excellent introduction to molecular biology with computer scientists and information engineering researchers in mind, laying out the basic biological knowledge behind the application of clustering analysis techniques in bioinformatics
Explains the structure and properties of many types of high-throughput datasets commonly found in biological studies
Discusses how clustering methods and their possible successors would be used to enhance the pace of biological discoveries in the future
Includes a companion website hosting a selected collection of codes and links to publicly available datasets
Table of Contents
Preface xix
List of Symbols xxi
About the Authors xxiii
Part One Introduction 1
1 Introduction to Bioinformatics 3
2 Computational Methods in Bioinformatics 9
Part Two Introduction to Molecular Biology 19
3 The Living Cell 21
4 Central Dogma of Molecular Biology 33
Part Three Data Acquisition and Pre-processing 53
5 High-throughput Technologies 55
6 Databases, Standards and Annotation 67
7 Normalisation 87
8 Feature Selection 109
9 Differential Expression 119
Part Four Clustering Methods 133
10 Clustering Forms 135
11 Partitional Clustering 143
12 Hierarchical Clustering 157
13 Fuzzy Clustering 167
14 Neural Network-based Clustering 181
15 Mixture Model Clustering 197
16 Graph Clustering 227
17 Consensus Clustering 247
18 Biclustering 265
19 Clustering Methods Discussion 283
Part Five Validation and Visualisation 303
20 Numerical Validation 305
21 Biological Validation 323
22 Visualisations and Presentations 339
Part Six New Clustering Frameworks Designed for Bioinformatics 363
23 Splitting-Merging Awareness Tactics (SMART) 365
24 Tightness-tunable Clustering (UNCLES) 385
Appendix 395
Index 409
by "Nielsen BookData"
