Data analysis and visualization in genomics and proteomics / editors, Francisco Azuaje and Joaquín Dopazo.

Format:

• Book

Contributor:

• Azuaje, Francisco.
• Dopazo, Joaquín.
• Class of 1924 Book Fund.

Language:

English

Subjects (All):

• Genomics--Data processing.
• Proteomics--Data processing.
• Data mining.
• Genomics--statistics & numerical data.
• Proteomics--statistics & numerical data.
• Systems Biology.
• Proteomics.
• Genomics.

Medical Subjects:

• Genomics--statistics & numerical data.
• Proteomics--statistics & numerical data.
• Systems Biology.

Physical Description:

xv, 267 pages : illustrations ; 25 cm

Place of Publication:

Hoboken, NJ : John Wiley, [2005]

Summary:

• This book provides scientists and students with the basis for the development of integrative computational approaches to analysing biological data on a systemic scale. It emphasises the processing of multiple data and knowledge resources, and the combination of different prediction models and systems. It covers different data analysis and visualization techniques for studying the roles of genes and proteins at a systems level. A fairly broad definition for the areas of genomics and proteomics is adopted, which also encompasses a wider spectrum of 'omic' approaches required to understand the functions of genes and their products.
• From a bioinformatics point of view, the book illustrates: how data analysis techniques can facilitate more comprehensive, user-friendly data visualization tasks; how data visualization methods may make data analysis a more meaningful and biologically relevant process; and how to approach the overabundance of data in genomic studies, in which spurious associations often occur, with the proper statistical tools. The book describes how this synergy may support integrative approaches to functional genomics. The book will be of interest to all bioinformaticians, from students to researchers, as well as to many scientists working in genomics, proteomics, systems biology and related areas.

Contents:

• Section I Introduction - Data Diversity and Integration 1
• 1 Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges / Francisco Azuaje, Joaquin Dopazo 3
• 1.1 Data Analysis and Visualization: An Integrative Approach 3
• 1.2 Critical Design and Implementation Factors 5
• 2 Biological Databases: Infrastructure, Content and Integration / Allyson L. Williams, Paul J. Kersey, Manuela Pruess, Rolf Apweiler 11
• 2.2 Data Integration 12
• 2.3 Review of Molecular Biology Databases 17
• 3 Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions / Francisco Azuaje, Joaquin Dopazo, Haiying Wang 29
• 3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29
• 3.2 Integrating Informational Views and Complexity for Understanding Function 31
• 3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis 34
• Section II Integrative Data Mining and Visualization - Emphasis on Combination of Multiple Data Types 41
• 4 Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps / Martin Krallinger, Alfonso Valencia 43
• 4.2 Introduction to Text Mining and NLP 45
• 4.3 Databases and Resources for Biomedical Text Mining 47
• 4.4 Text Mining and Protein-Protein Interactions 50
• 4.5 Other Text-Mining Applications in Genomics 55
• 4.6 The Future of NLP in Biomedicine 56
• 5 Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis / Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Falk Schubert, Mark Gerstein 61
• 5.2 Genomic Features in Protein Interaction Predictions 63
• 5.3 Machine Learning on Protein-Protein Interactions 67
• 5.4 The Missing Value Problem 73
• 5.5 Network Analysis of Protein Interactions 75
• 6 Integration of Genomic and Phenotypic Data / Amanda Clare 83
• 6.1 Phenotype 83
• 6.2 Forward Genetics and QTL Analysis 85
• 6.3 Reverse Genetics 87
• 6.4 Prediction of Phenotype from Other Sources of Data 88
• 6.5 Integrating Phenotype Data with Systems Biology 90
• 6.6 Integration of Phenotype Data in Databases 93
• 7 Ontologies and Functional Genomics / Fatima Al-Shahrour, Joaquin Dopazo 99
• 7.1 Information Mining in Genome-Wide Functional Analysis 99
• 7.2 Sources of Information: Free Text Versus Curated Repositories 100
• 7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101
• 7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge 103
• 7.5 Statistical Approaches to Test Significant Biological Differences 104
• 7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes 106
• 7.7 Other Tools 107
• 7.8 Examples of Functional Analysis of Clusters of Genes 108
• 7.9 Future Prospects 110
• 8 The C. elegans Interactome: its Generation and Visualization / Alban Chesnau, Claude Sardet 113
• 8.2 The ORFeome: the first step toward the interactome of C. elegans 116
• 8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects 118
• 8.4 Visualization and Topology of Protein-Protein Interaction Networks 121
• 8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets 123
• 8.6 Conclusion: From Interactions to Therapies 129
• Section III Integrative Data Mining and Visualization - Emphasis on Combination of Multiple Prediction Models and Methods 135
• 9 Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions / Steve R. Pettifer, James R. Sinnott, Teresa K. Attwood 137
• 9.2 Sequence Analysis Methods and Databases 139
• 9.3 A View Through a Portal 141
• 9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142
• 9.5 A Toolkit View 143
• 9.6 Challenges and Opportunities 145
• 9.7 Extending the Desktop Metaphor 147
• 10 Advances in Cluster Analysis of Microarray Data / Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal, Bart De Moor 153
• 10.2 Some Preliminaries 155
• 10.3 Hierarchical Clustering 157
• 10.4 k-Means Clustering 159
• 10.5 Self-Organizing Maps 159
• 10.6 A Wish List for Clustering Algorithms 160
• 10.7 The Self-Organizing Tree Algorithm 161
• 10.8 Quality-Based Clustering Algorithms 162
• 10.9 Mixture Models 163
• 10.10 Biclustering Algorithms 166
• 10.11 Assessing Cluster Quality 168
• 10.12 Open Horizons 170
• 11 Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery / Olga G. Troyanskaya 175
• 11.1 Functional Genomics: Goals and Data Sources 175
• 11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data 177
• 11.3 Integration of Diverse Functional Data For Accurate Gene Function Prediction 179
• 11.4 MAGIC - General Probabilistic Integration of Diverse Genomic Data 180
• 12 Supervised Methods with Genomic Data: a Review and Cautionary View / Ramon Diaz-Uriarte 193
• 12.2 Class Prediction and Class Comparison 194
• 12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194
• 12.4 Class Prediction and Prognostic Prediction 198
• 12.5 ROC Curves for Evaluating Predictors and Differential Expression 201
• 12.6 Caveats and Admonitions 203
• 12.7 Final Note: Source Code Should be Available 209
• 13 A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models / Pedro Larranaga, Inaki Inza, Jose L. Flores 215
• 13.2 Genetic Networks 216
• 13.3 Probabilistic Graphical Models 218
• 13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229
• 14 Integrative Models for the Prediction and Understanding of Protein Structure Patterns / Inge Jonassen 239
• 14.2 Structure Prediction 241
• 14.3 Classifications of Structures 244
• 14.4 Comparing Protein Structures 246
• 14.5 Methods for the Discovery of Structure Motifs 249.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Class of 1924 Book Fund.

ISBN:

0470094397

OCLC:

58563657

Online:

The Class of 1924 Book Fund Home Page