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Integrative Cluster Analysis in Bioinformatics

  • ID: 2899031
  • Book
  • 448 Pages
  • John Wiley and Sons Ltd
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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
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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

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Basel Abu–Jamous
Rui Fa
Asoke K. Nandi
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