Biological Database Modeling -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction to Data Modeling -- 1.1 Generic Modern Markup Languages -- 1.2 Modeling Complex Data Structures -- 1.3 Data Modeling with General Markup Languages -- 1.4 Ontologies: Enriching Data with Text -- 1.5 Hyperlinks for Semantic Modeling -- 1.6 Evolving Subject Indexes -- 1.7 Languages -- 1.8 Views -- 1.9 Modeling Biological Data -- Chapter 2 Public Biological Databases for -Omics Studies in Medicine -- 2.1 Introduction -- 2.2 Public Databases in Medicine -- 2.3 Application of Public Bioinformatics Database in Medicine -- Chapter 3 Modeling Biomedical Data -- 3.1 Introduction -- 3.2 Biological Concepts and EER Modeling -- 3.3 Formal Definitions for EER Extensions -- 3.4 Summary of New EER Notation -- 3.5 Semantic Data Models of the Molecular Biological System -- 3.6 EER-to-Relational Mapping -- 3.7 Introduction to Multilevel Modeling and Data Source Integration -- 3.8 Multilevel Concepts and EER Modeling -- 3.9 Conclusion -- Chapter 4 Fundamentals of Gene Ontology -- 4.1 Introduction to Gene Ontology -- 4.2 Construction of an Ontology -- 4.3 General Evolution of GO Structures and General Annotation Strategy ofAssigning GO Terms to Genes -- 4.4 Applications of Gene Ontology in Biological and Medical Science -- Chapter 5 Protein Ontology -- 5.1 Introduction -- 5.2 What Is Protein Annotation? -- 5.3 Underlying Issues with Protein Annotation -- 5.4 Developing Protein Ontology -- 5.5 Protein Ontology Framework -- 5.6 Protein Ontology Instance Store -- 5.7 Strengths and Limitations of Protein Ontology -- 5.8 Summary -- Chapter 6 Information Quality Management Challenges for High-Throughput Data -- 6.1 Motivation -- 6.2 The Experimental Context -- 6.3 A Survey of Quality Issues -- 6.4 Current Approaches to Quality -- 6.5 Conclusions.

Chapter 7 Data Management for Fungal Genomics: An Experience Report -- 7.1 Introduction -- 7.2 Materials Tracking Database -- 7.3 Annotation Database -- 7.4 Microarray Database -- 7.5 Target Curation Database -- 7.6 Discussion -- 7.7 Conclusion -- Chapter 8 Microarray Data Management: An Enterprise Information Approach -- 8.1 Introduction -- 8.2 Microarray Data Standardization -- 8.3 Database Management Systems -- 8.4 Microarray Data Storage and Exchange -- 8.5 Challenges and Conside -- 8.6 Conclusions -- Chapter 9 Data Management in Expression-Based Proteomics -- 9.1 Background -- 9.2 Proteomics Data Management Approaches -- 9.3 Data Standards in Mass Spectrometry Based Proteomics Studies -- 9.4 Public Repositories for Mass Spectrometry Data -- 9.5 Proteomics Data Management Tools -- 9.6 Expression Proteomics in the Context of Systems Biology Studies -- 9.7 Protein Annotation Databases -- 9.8 Conclusions -- Chapter 10 Model-Driven Drug Discovery: Principles and Practices -- 10.1 Introduction -- 10.2 Model Abstraction -- 10.3 Target Identification -- 10.4 Lead Identification -- 10.5 Lead to Drug Phase -- 10.6 Future Perspectives -- Chapter 11 Information Management and Interaction in High-Throughput Screeningfor Drug Discovery -- 11.1 Introduction -- 11.2 Prior Research -- 11.3 Overview of Antimalarial Drug Discovery -- 11.4 Overview of the Proposed Solution and System Architecture -- 11.5 HTS Data Processing -- 11.6 Data Modeling -- 11.7 User Interface -- 11.8 Conclusions -- About the Authors -- Index.