Data Analysis and Visualization in Genomics and Proteomics 1st Edition by Francisco Azuaje, Joaquin Dopazo ISBN 0470094397 978-0470094396

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Product details: 

ISBN 10: 0470094397 

ISBN 13: 978-0470094396

Author: Francisco Azuaje, Joaquin Dopazo

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

Table of contents: 

SECTION I: INTRODUCTION – DATA DIVERSITY AND INTEGRATION

1. Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges (Francisco Azuaje and Joaquín Dopazo)
1.1 Data Analysis and Visualization: An Integrative Approach
1.2 Critical Design and Implementation Factors
1.3 Overview of Contributions
References

2. Biological Databases: Infrastructure, Content and Integration (Allyson L. Williams, Paul J. Kersey, Manuela Pruess and Rolf Apweiler)
2.1 Introduction
2.2 Data Integration
2.3 Review of Molecular Biology Databases
2.4 Conclusion
References

3. Data and Predictive Model Integration: An Overview of Key Concepts, Problems and Solutions (Francisco Azuaje, Joaquín Dopazo and Haiying Wang)
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. Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps (Martin Krallinger and Alfonso Valencia)
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. 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 and Mark Gerstein)
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. Integration of Genomic and Phenotypic Data (Amanda Clare)
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. Ontologies and Functional Genomics (Fátima Al-Shahrour and Joaquín Dopazo)
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. The C. elegans Interactome: Its Generation and Visualization (Alban Chesnau and Claude Sardet)
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

9. Integrated Approaches for Bioinformatic Data Analysis and Visualization – Challenges, Opportunities and New Solutions (Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood)
9.1 Introduction
9.2 Sequence Analysis Methods and Databases
9.3 A View Through a Portal
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All
9.5 A Toolkit View
9.6 Challenges and Opportunities
9.7 Extending the Desktop Metaphor
9.8 Conclusions
Acknowledgements
References

10. Advances in Cluster Analysis of Microarray Data (Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal and Bart De Moor)
10.1 Introduction
10.2 Some Preliminaries
10.3 Hierarchical Clustering
10.4 k-Means Clustering
10.5 Self-Organizing Maps
10.6 A Wish List for Clustering Algorithms
10.7 The Self-Organizing Tree Algorithm
10.8 Quality-Based Clustering Algorithms
10.9 Mixture Models
10.10 Biclustering Algorithms
10.11 Assessing Cluster Quality
10.12 Open Horizons
References

11. Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery (Olga G. Troyanskaya)
11.1 Functional Genomics: Goals and Data Sources
11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data
11.3 Integration of Diverse Functional Data for Accurate Gene Function Prediction
11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data
11.5 Conclusion
References

12. Supervised Methods with Genomic Data: A Review and Cautionary View (Ramón Díaz-Uriarte)
12.1 Chapter Objectives
12.2 Class Prediction and Class Comparison
12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes
12.4 Class Prediction and Prognostic Prediction
12.5 ROC Curves for Evaluating Predictors and Differential Expression
12.6 Caveats and Admonitions
12.7 Final Note: Source Code Should be Available
Acknowledgements
References

13. A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models (Pedro Larrañaga, Iñaki Inza and Jose L. Flores)
13.1 Introduction
13.2 Genetic Networks
13.3 Probabilistic Graphical Models
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models
13.5 Conclusions
Acknowledgements
References

14. Integrative Models for the Prediction and Understanding of Protein Structure Patterns (Inge Jonassen)
14.1 Introduction
14.2 Structure Prediction
14.3 Classifications of Structures
14.4 Comparing Protein Structures
14.5 Methods for the Discovery of Structure Motifs
14.6 Discussion and Conclusions

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Tags: Francisco Azuaje, Joaquin Dopazo, Data Analysis, Genomics and Proteomics