Clustering in Bioinformatics and Drug Discovery : Chapman & Hall/CRC Computational Biology Series - John David MacCuish

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Clustering in Bioinformatics and Drug Discovery

Name: Clustering in Bioinformatics and Drug Discovery
Brand: Taylor & Francis Inc
Price: 166.25 AUD
Availability: BackOrder

By: John David MacCuish, Norah E. MacCuish

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Hardcover | 15 November 2010 | Edition Number 1

At a Glance

Hardcover
244 Pages

Dimensions(cm)
24.3 x 16.2 x 1.8

Hardcover

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With a DVD of color figures, Clustering in Bioinformatics and Drug Discovery provides an expert guide on extracting the most pertinent information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and drug discovery.

Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on partitional, cluster sampling, and hierarchical algorithms, the book provides readers with enough detail to obtain a basic understanding of cluster analysis for bioinformatics and drug discovery. The remaining chapters cover more advanced methods, such as hybrid and parallel algorithms, as well as details related to specific types of data, including asymmetry, ambiguity, validation measures, and visualization.

This book explores the application of cluster analysis in the areas of bioinformatics and cheminformatics as they relate to drug discovery. Clarifying the use and misuse of clustering methods, it helps readers understand the relative merits of these methods and evaluate results so that useful hypotheses can be developed and tested.

Industry Reviews

John trained in computer science and has been involved with data mining and statistical analysis; Norah trained as a theoretical physical chemist and has mostly worked for pharmaceutical companies on drug discovery. They run a company that merges their fields, and it is that overlap that they describe here. They explain how cluster analysis, an exploratory data analysis tool, is used in bioinformatics and cheminformatics as they relate to drug discovery. The goal is for practitioners to be aware of the relative merits of clustering methods with the data they have at hand. --SciTech Book News, February 2011 ! In this volume, the authors present sufficient options so that the user can choose the appropriate method for their data. ! Practitioners in the pharmaceutical industry need an expert guide, which the authors of this book provide, to extract the most information from their data. Those of us who learned their clustering from Anderberg, Sokal and Sneath, and Willett now have a valuable additional resource suitable for the 21st century. --From the Foreword by John Bradshaw, Barley, Hertfordshire, UK

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List of Figures
List of Tables
Preface
Acknowledgments
About the Authors
List of Symbols
Foreword
Introduction	p. 1
History	p. 8
Bioinformatics and Drug Discovery	p. 13
Statistical Learning Theory and Exploratory Data Analysis	p. 16
Clustering Algorithms	p. 17
Computational Complexity	p. 18
Data Structures	p. 21
Parallel Algorithms	p. 23
Glossary	p. 24
Exercises	p. 24
Data	p. 27
Types	p. 27
Binary Data	p. 28
Count Data	p. 31
Continuous Data	p. 32
Categorical Data	p. 32
Mixed Type Data	p. 32
Normalization and Scaling	p. 33
Transformations	p. 33
Formats	p. 36
Data Matrices	p. 36
Measures of Similarity	p. 37
Binary Data Measures	p. 38
The Tanimoto or Jaccard Measure	p. 39
The Baroni-Urbani/Buser Coefficient	p. 41
The Simple Matching Coefficient	p. 42
The Binary Euclidean Measure	p. 46
The Binary Cosine or Ochai Measure	p. 46
The Hamann Measure	p. 47
Other Binary Measures	p. 49
Count Data Measures	p. 49
The Tanimoto Count Measure	p. 50
The Cosine Count Measure	p. 50
Continuous Data Measures	p. 50
Continuous and Weighted Forms of Euclidean Distance	p. 50
Manhattan Distance	p. 51
L∞ or Supremum Norm	p. 51
Cosine	p. 51
Pearson Correlation Coefficient	p. 52
Mahalanobis Distance	p. 52
Mixed Type Data	p. 53
Proximity Matrices	p. 53
Symmetric Matrices	p. 53
Asymmetric Matrices	p. 55
Hadamard Product of Two Matrices	p. 56
Ultrametricity	p. 57
Positive Semidefinite Matrices	p. 57
Dimensionality, Components, Discriminants	p. 57
Principal Component Analysis (PCA)	p. 58
Covariance Matrix	p. 58
Singular Value Decomposition	p. 59
Non-Negative Matrix Factorization	p. 59
Multidimensional Scaling	p. 59
Discriminants	p. 60
Fisher's Linear Discriminant	p. 60
Graph Theory	p. 60
Glossary	p. 65
Exercises	p. 66
Clustering Forms	p. 69
Partitional	p. 71
Hierarchical	p. 73
Dendrograms and Heatmaps	p. 73
Mixture Models	p. 80
Sampling	p. 81
Overlapping	p. 82
Fuzzy	p. 83
Self-Organizing	p. 84
Hybrids	p. 85
Glossary	p. 85
Exercises	p. 86
Partitional Algorithms	p. 91
K-Means	p. 91
K-Medoid	p. 93
K-Modes	p. 94
Online K-Means	p. 94
Jarvis-Patrick	p. 95
Spectral Clustering	p. 98
Self-Organizing Maps	p. 99
Glossary	p. 101
Exercises	p. 101
Cluster Sampling Algorithms	p. 103
Leader Algorithms	p. 103
Taylor-Butina Algorithm	p. 107
Glossary	p. 109
Exercises	p. 112
Hierarchical Algorithms	p. 113
Agglomerative	p. 113
Reciprocal Nearest Neighbors Class of Algorithms	p. 114
Complete Link	p. 114
Group Average	p. 114
Wards	p. 115
Others	p. 116
Divisive	p. 119
Glossary	p. 119
Exercises	p. 120
Hybrid Algorithms	p. 121
Self-Organizing Tree Algorithm	p. 121
Divisive Hierarchical K-Means	p. 123
Exclusion Region Hierarchies	p. 124
Biclustering	p. 126
Glossary	p. 126
Exercises	p. 127
Asymmetry	p. 129
Measures	p. 132
Tversky	p. 132
Edit Distance	p. 133
Algorithms	p. 134
Strongly Connected Components Agglomerative Hierarchical Algorithm	p. 134
Modified Taylor-Butina Algorithm	p. 134
Glossary	p. 135
Exercises	p. 136
Ambiguity	p. 137
Discrete Valued Data Types	p. 138
Precision	p. 138
Ties in Proximity	p. 139
Measure Probability and Distributions	p. 140
Number Theory in Measures	p. 140
Algorithm Decision Ambiguity	p. 145
Overlapping Clustering Algorithms Based on Ambiguity	p. 148
Modified Taylor-Butina Algorithm	p. 148
Jarvis-Patrick	p. 150
K-Means	p. 151
Hierarchical or Pyramidal Algorithms	p. 152
Glossary	p. 152
Exercises	p. 152
Validation 155
Validation Measures	p. 156
Internal Cluster Measures	p. 56
External Cluster Measures	p. 157
Uses of External Cluster Measures	p. 158
Tendency to Cluster	p. 159
Application Dependence and Domain Knowledge	p. 159
Monte Carlo Analysis	p. 160
Visualization	p. 161
Example	p. 162
Glossary	p. 166
Exercises	p. 166
Large Scale and Parallel Algorithms	p. 169
Leader and Leader-Follower Algorithms	p. 170
Taylor-Butina	p. 171
K-Means and Variants	p. 172
Divisive K-Means	p. 173
Examples	p. 174
Compound Diversity	p. 174
Compound Acquisition	p. 176
Glossary	p. 179
Exercises	p. 179
Appendices	p. 181
Primer on Matrix Algebra	p. 181
Primer on Probability Theory	p. 188
Primer on Number Theory	p. 189
Bibliography	p. 193
Index	p. 205
Table of Contents provided by Ingram. All Rights Reserved.

Clustering in Bioinformatics and Drug Discovery

At a Glance

Hardcover

Industry Reviews

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