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Produktbild: Analysis of DNA Microarrays

Analysis of DNA Microarrays

Aus der Reihe Wiley Series in Bioinformatics

Fr. 147.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.04.2013

Herausgeber

Yi Pan + weitere

Verlag

John Wiley & Sons

Seitenzahl

736

Maße (L/B/H)

24/16.1/4.5 cm

Gewicht

1285 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-17081-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.04.2013

Herausgeber

Verlag

John Wiley & Sons

Seitenzahl

736

Maße (L/B/H)

24/16.1/4.5 cm

Gewicht

1285 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-17081-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Analysis of DNA Microarrays

  • Preface xix

    Abbreviations xxiii

    1 Introduction 1

    1.1 Class Discovery 2

    1.2 Dimensional Reduction 4

    1.3 Class Prediction 4

    1.4 Classification Rules of Thumb 5

    1.5 DNA Microarray Datasets Used 9

    References 11

    Part I Class Discovery 13

    2 Crisp K-Means Cluster Analysis 15

    2.1 Introduction 15

    2.2 Algorithm 16

    2.3 Implementation 18

    2.4 Distance Metrics 20

    2.5 Cluster Validity 24

    2.5.1 Davies-Bouldin Index 25

    2.5.2 Dunn's Index 25

    2.5.3 Intracluster Distance 26

    2.5.4 Intercluster Distance 27

    2.5.5 Silhouette Index 30

    2.5.6 Hubert's Statistic 31

    2.5.7 Randomization Tests for Optimal Value of K 31

    2.6 V-Fold Cross-Validation 35

    2.7 Cluster Initialization 37

    2.7.1 K Randomly Selected Microarrays 37

    2.7.2 K Random Partitions 40

    2.7.3 Prototype Splitting 41

    2.8 Cluster Outliers 44

    2.9 Summary 44

    References 45

    3 Fuzzy K-Means Cluster Analysis 47

    3.1 Introduction 47

    3.2 Fuzzy K-Means Algorithm 47

    3.3 Implementation 49

    3.4 Summary 54

    References 54

    4 Self-Organizing Maps 57

    4.1 Introduction 57

    4.2 Algorithm 57

    4.2.1 Feature Transformation and Reference Vector Initialization 59

    4.2.2 Learning 60

    4.2.3 Conscience 61

    4.3 Implementation 63

    4.3.1 Feature Transformation and Reference Vector Initialization 63

    4.3.2 Reference Vector Weight Learning 66

    4.4 Cluster Visualization 67

    4.4.1 Crisp K-Means Cluster Analysis 67

    4.4.2 Adjacency Matrix Method 68

    4.4.3 Cluster Connectivity Method 69

    4.4.4 Hue-Saturation-Value (HSV) Color Normalization 69

    4.5 Unified Distance Matrix (U Matrix) 71

    4.6 Component Map 71

    4.7 Map Quality 73

    4.8 Nonlinear Dimension Reduction 75

    References 79

    5 Unsupervised Neural Gas 81

    5.1 Introduction 81

    5.2 Algorithm 82

    5.3 Implementation 82

    5.3.1 Feature Transformation and Prototype Initialization 82

    5.3.2 Prototype Learning 83

    5.4 Nonlinear Dimension Reduction 85

    5.5 Summary 87

    References 88

    6 Hierarchical Cluster Analysis 91

    6.1 Introduction 91

    6.2 Methods 91

    6.2.1 General Programming Methods 91

    6.2.2 Step 1: Cluster-Analyzing Arrays as Objects with Genes as Attributes 92

    6.2.3 Step 2: Cluster-Analyzing Genes as Objects with Arrays as Attributes 94

    6.3 Algorithm 96

    6.4 Implementation 96

    6.4.1 Heatmap Color Control 96

    6.4.2 User Choices for Clustering Arrays and Genes 97

    6.4.3 Distance Matrices and Agglomeration Sequences 98

    6.4.4 Drawing Dendograms and Heatmaps 104

    References 105

    7 Model-Based Clustering 107

    7.1 Introduction 107

    7.2 Algorithm 110

    7.3 Implementation 111

    7.4 Summary 116

    References 117

    8 Text Mining: Document Clustering 119

    8.1 Introduction 119

    8.2 Duo-Mining 119

    8.3 Streams and Documents 120

    8.4 Lexical Analysis 120

    8.4.1 Automatic Indexing 120

    8.4.2 Removing Stopwords 121

    8.5 Stemming 121

    8.6 Term Weighting 121

    8.7 Concept Vectors 124

    8.8 Main Terms Representing Concept Vectors 124

    8.9 Algorithm 125

    8.10 Preprocessing 127

    8.11 Summary 137

    References 137

    9 Text Mining: N-Gram Analysis 139

    9.1 Introduction 139

    9.2 Algorithm 140

    9.3 Implementation 141

    9.4 Summary 154

    References 156

    Part II Dimension Reduction 159

    10 Principal Components Analysis 161

    10.1 Introduction 161

    10.2 Multivariate Statistical Theory 161

    10