Francisco Azuaje and Joaquin DopazoAllyson L. Williams and Paul J. Kersey and Manuela Pruess and Rolf ApweilerFrancisco Azuaje and Joaquin Dopazo and Haiying WangMartin Krallinger and Alfonso ValenciaLong J. Lu and Yu Xia and Haiyuan Yu and Alexander Rives and Haoxin Lu and Falk Schubert and Mark GersteinAmanda ClareFatima Al-Shahrour and Joaquin DopazoAlban Chesnau and Claude SardetSteve R. Pettifer and James R. Sinnott and Teresa K. AttwoodQizheng Sheng and Yves Moreau and Frank De Smet and Kathleen Marchal and Bart De MoorOlga G. TroyanskayaRamon Diaz-UriartePedro Larranaga and Inaki Inza and Jose L. FloresInge Jonassen

Preface	p. xi
List of Contributors	p. xiii
Section I Introduction - Data Diversity and Integration	p. 1
1 Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges	p. 3
1.1 Data Analysis and Visualization: An Integrative Approach	p. 3
1.2 Critical Design and Implementation Factors	p. 5
1.3 Overview of Contributions	p. 8
References	p. 9
2 Biological Databases: Infrastructure, Content and Integration	p. 11
2.1 Introduction	p. 11
2.2 Data Integration	p. 12
2.3 Review of Molecular Biology Databases	p. 17
2.4 Conclusion	p. 23
References	p. 26
3 Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions	p. 29
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches	p. 29
3.2 Integrating Informational Views and Complexity for Understanding Function	p. 31
3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis	p. 34
3.4 Final Remarks	p. 36
References	p. 38
Section II Integrative Data Mining and Visualization - Emphasis on Combination of Multiple Data Types	p. 41
4 Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps	p. 43
4.1 Introduction	p. 44
4.2 Introduction to Text Mining and NLP	p. 45
4.3 Databases and Resources for Biomedical Text Mining	p. 47
4.4 Text Mining and Protein-Protein Interactions	p. 50
4.5 Other Text-Mining Applications in Genomics	p. 55
4.6 The Future of NLP in Biomedicine	p. 56
Acknowledgements	p. 56
References	p. 56
5 Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis	p. 61
5.1 Introduction	p. 62
5.2 Genomic Features in Protein Interaction Predictions	p. 63
5.3 Machine Learning on Protein-Protein Interactions	p. 67
5.4 The Missing Value Problem	p. 73
5.5 Network Analysis of Protein Interactions	p. 75
5.6 Discussion	p. 79
References	p. 80
6 Integration of Genomic and Phenotypic Data	p. 83
6.1 Phenotype	p. 83
6.2 Forward Genetics and QTL Analysis	p. 85
6.3 Reverse Genetics	p. 87
6.4 Prediction of Phenotype from Other Sources of Data	p. 88
6.5 Integrating Phenotype Data with Systems Biology	p. 90
6.6 Integration of Phenotype Data in Databases	p. 93
6.7 Conclusions	p. 95
References	p. 95
7 Ontologies and Functional Genomics	p. 99
7.1 Information Mining in Genome-Wide Functional Analysis	p. 99
7.2 Sources of Information: Free Text Versus Curated Repositories	p. 100
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics	p. 101
7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge	p. 103
7.5 Statistical Approaches to Test Significant Biological Differences	p. 104
7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes	p. 106
7.7 Other Tools	p. 107
7.8 Examples of Functional Analysis of Clusters of Genes	p. 108
7.9 Future Prospects	p. 110
References	p. 110
8 The C. elegans Interactome: its Generation and Visualization	p. 113
8.1 Introduction	p. 113
8.2 The ORFeome: the first step toward the interactome of C. elegans	p. 116
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects	p. 118
8.4 Visualization and Topology of Protein-Protein Interaction Networks	p. 121
8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets	p. 123
8.6 Conclusion: From Interactions to Therapies	p. 129
References	p. 130
Section III Integrative Data Mining and Visualization - Emphasis on Combination of Multiple Prediction Models and Methods	p. 135
9 Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions	p. 137
9.1 Introduction	p. 137
9.2 Sequence Analysis Methods and Databases	p. 139
9.3 A View Through a Portal	p. 141
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All	p. 142
9.5 A Toolkit View	p. 143
9.6 Challenges and Opportunities	p. 145
9.7 Extending the Desktop Metaphor	p. 147
9.8 Conclusions	p. 151
Acknowledgements	p. 151
References	p. 152
10 Advances in Cluster Analysis of Microarray Data	p. 153
10.1 Introduction	p. 153
10.2 Some Preliminaries	p. 155
10.3 Hierarchical Clustering	p. 157
10.4 k-Means Clustering	p. 159
10.5 Self-Organizing Maps	p. 159
10.6 A Wish List for Clustering Algorithms	p. 160
10.7 The Self-Organizing Tree Algorithm	p. 161
10.8 Quality-Based Clustering Algorithms	p. 162
10.9 Mixture Models	p. 163
10.10 Biclustering Algorithms	p. 166
10.11 Assessing Cluster Quality	p. 168
10.12 Open Horizons	p. 170
References	p. 171
11 Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery	p. 175
11.1 Functional Genomics: Goals and Data Sources	p. 175
11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data	p. 177
11.3 Integration of Diverse Functional Data For Accurate Gene Function Prediction	p. 179
11.4 MAGIC - General Probabilistic Integration of Diverse Genomic Data	p. 180
11.5 Conclusion	p. 188
References	p. 189
12 Supervised Methods with Genomic Data: a Review and Cautionary View	p. 193
12.1 Chapter Objectives	p. 193
12.2 Class Prediction and Class Comparison	p. 194
12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes	p. 194
12.4 Class Prediction and Prognostic Prediction	p. 198
12.5 ROC Curves for Evaluating Predictors and Differential Expression	p. 201
12.6 Caveats and Admonitions	p. 203
12.7 Final Note: Source Code Should be Available	p. 209
Acknowledgements	p. 210
References	p. 210
13 A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models	p. 215
13.1 Introduction	p. 215
13.2 Genetic Networks	p. 216
13.3 Probabilistic Graphical Models	p. 218
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models	p. 229
13.5 Conclusions	p. 234
Acknowledgements	p. 235
References	p. 235
14 Integrative Models for the Prediction and Understanding of Protein Structure Patterns	p. 239
14.1 Introduction	p. 239
14.2 Structure Prediction	p. 241
14.3 Classifications of Structures	p. 244
14.4 Comparing Protein Structures	p. 246
14.5 Methods for the Discovery of Structure Motifs	p. 249
14.6 Discussion and Conclusions	p. 252
References	p. 254
Index	p. 257