Microarray Measurements to Analyses

3.5 Background on Fold 98

3.5.1 Fold calculation and significance 100

3.5.2 Fold change may not mean the same thing in different expression measurement technologies 102

3.6 Dissimilarity and Similarity Measures 104

3.6.1 Linear correlation 105

3.6.2 Entropy and mutual information 106

3.6.3 Dynamics 111

Chapter 4: Genomic Data-Mining Techniques 114

4.1 Introduction 114

4.2 What Can Be Clustered in Functional Genomics? 114

4.3 What Does it Mean to Cluster? 115

4.4 Hierarchy of Bioinformatics Algorithms Available in Functional Genomics 115

4.5 Data Reduction and Filtering 118

4.5.1 Variation filter 119

4.5.2 Low entropy filter 119

4.5.3 Minimum expression level filter 122

4.5.4 Target ambiguity filter 122

4.6 Self-Organizing Maps 123

4.6.1 K-means clustering 127

4.7 Finding Genes That Split Sets 129

4.8 Phylogenetic-Type Trees 131

4.8.1 Two-dimensional dendrograms 135

4.9 Relevance Networks 137

4.10 Other Methods 144

4.11 Which Technique Should I Use?! 145

4.12 Determining the Significance of Findings; 148

4.12.1 Permutation testing 148

4.12.2 Testing and training sets 149

4.12.3 Performance metrics 151

4.12.4 Receiver operating characteristic curves 152

4.13 Genetic Networks 154

4.13.1 What is a genetic network? 154

4.13.2 Reverse-engineering and modeling a genetic network using limited data 154

4.13.3 Bayesian networks for functional genomics 157

Chapter 5: Bio-Ontologies, Data Models, Nomenclature 163

Overview 163

5.1 Ontologies 164

5.1.1 Bio-ontology projects 165

5.1.2 Advanced knowledge representation systems for bio-ontology 168

5.2 Expressivity versus Computability 169

5.3 Ontology versus Data Model versus Nomenclature 171

5.3.1 Exploiting the explicit and implicit ontologies of the biomedical literature 172

5.4 Data Model Introduction 176

5.5 Nomenclature 181

5.5.1 The unique gene identifier 184

5.6 Postanalysis Challenges 187

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