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Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems.
One of the first systematic overviews of the problem of biological data integration using computational approaches
This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale
Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems
Auteur
Dr Francisco Azuaje, Faculty of Informatics, University of Ulster, Jordanstown, Northern Ireland. Dr.?Joaquin Dopazo, Head of Bioinformatics, Spanish National Cancer Centre, Madrid, Spain.
Texte du rabat
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 visualisation 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 visualisation tasks; how data visualisation 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.
Résumé
Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems.
Contenu
Preface.
List of Contributors.
SECTION I: INTRODUCTION - DATA DIVERSITY AND INTEGRATION.
1.1 Data Analysis and Visualization: An Integrative Approach.
1.2 Critical Design and Implementation Factors.
1.3 Overview of Contributions.
References.
2.1 Introduction.
2.2 Data Integration.
2.3 Review of Molecular Biology Databases.
2.4 Conclusion.
References.
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches.
3.2 Integrating Informational Views and Complexity for Understanding Function.
3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis.
3.4 Final Remarks.
References.
SECTION II: INTEGRATIVE DATA MINING AND VISUALIZATION -EMPHASIS ON COMBINATION OF MULTIPLE DATA TYPES.
4.1 Introduction.
4.2 Introduction to Text Mining and NLP.
4.3 Databases and Resources for Biomedical Text Mining.
4.4 Text Mining and Protein-Protein Interactions.
4.5 Other Text-Mining Applications in Genomics.
4.6 The Future of NLP in Biomedicine.
Acknowledgements.
References.
5.1 Introduction.
5.2 Genomic Features in Protein Interaction Predictions.
5.3 Machine Learning on Protein-Protein Interactions.
5.4 The Missing Value Problem.
5.5 Network Analysis of Protein Interactions.
5.6 Discussion.
References.
6.1 Phenotype.
6.2 Forward Genetics and QTL Analysis.
6.3 Reverse Genetics.
6.4 Prediction of Phenotype from Other Sources of Data.
6.5 Integrating Phenotype Data with Systems Biology.
6.6 Integration of Phenotype Data in Databases.
6.7 Conclusions.
References.
7.1 Information Mining in Genome-Wide Functional Analysis.
7.2 Sources of Information: Free Text Versus Curated Repositories.
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics.
7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge.
7.5 Statistical Approaches to Test Significant Biological Differences.
7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes.
7.7 Other Tools.
7.8 Examples of Functional Analysis of Clusters of Genes.
7.9 Future Prospects.
References.
8.1 Introduction.
8.2 The ORFeome: the first step toward the interactome of C. elegans.
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects.
8.4 Visualization and Topology of Protein-Protein Interaction Networks.
8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets.
8.6 Conclusion: From Interactions to Therapies.
References.
SECTION III: INTEGRATIVE DATA MINING AND VISUALIZATION - EMPHASIS ON COMBINATION OF MULTIPLE PREDICTION MODELS AND METHODS.