Biological database modeling

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Publication Information:

Norwood, MA : Artech House, 2008

ISBN:

9781596932586

Subject Term:

Bioinformatics

Database management

Computational biology

Added Author:

Chen, Jake

Sidhu, Amandeep S.

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010230052	QH324.2 B5645 2008	Open Access Book	Book	Searching... Unknown
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Searching... PSZ JB	30000010178918	QH324.2 B5645 2008	Open Access Book	Book	Searching... Unknown

Modern biological research in areas like drug discovery produces a staggering volume of data, and the right modeling tools can help scientists apply it in ways never before imaginable. This collection of next-generation biodata modeling techniques combines innovative concepts, methods, and applications with case studies in genome, microarray, proteomics, and drug discovery projects that helps bioinformatics professionals develop ever-more powerful data management systems in any domain.

Preface	p. xiii
Acknowledgments	p. xvii
Chapter 1 Introduction to Data Modeling	p. 1
1.1 Generic Modern Markup Languages	p. 1
1.2 Modeling Complex Data Structures	p. 3
1.3 Data Modeling with General Markup Languages	p. 3
1.4 Ontologies: Enriching Data with Text	p. 4
1.5 Hyperlinks for Semantic Modeling	p. 5
1.6 Evolving Subject Indexes	p. 6
1.7 Languages	p. 6
1.8 Views	p. 7
1.9 Modeling Biological Data	p. 7
References	p. 8
Chapter 2 Public Biological Databases for -Omics Studies in Medicine	p. 9
2.1 Introduction	p. 9
2.2 Public Databases in Medicine	p. 10
2.3 Application of Public Bioinformatics Database in Medicine	p. 11
2.3.1 Application of Genomic Database	p. 11
2.3.2 Application of Proteomic Database	p. 16
2.3.3 Application of the Metabolomics Database	p. 18
2.3.4 Application of Pharmacogenomics Database	p. 19
2.3.5 Application of Systomics Database	p. 21
References	p. 21
Chapter 3 Modeling Biomedical Data	p. 25
3.1 Introduction	p. 25
3.2 Biological Concepts and EER Modeling	p. 27
3.2.1 Sequence Ordering Concept	p. 27
3.2.2 Input/Output Concept	p. 29
3.2.3 Molecular Spatial Relationship Concept	p. 30
3.3 Formal Definitions for EER Extensions	p. 31
3.3.1 Ordered Relationships	p. 31
3.3.2 Process Relationships	p. 33
3.3.3 Molecular Spatial Relationships	p. 34
3.4 Summary of New EER Notation	p. 35
3.5 Semantic Data Models of the Molecular Biological System	p. 35
3.5.1 The DNA/Gene Model	p. 36
3.5.2 The Protein 3D Structure Model	p. 36
3.5.3 The Molecular Interaction and Pathway Model	p. 40
3.6 EER-to-Relational Mapping	p. 41
3.6.1 Ordered Relationship Mapping	p. 41
3.6.2 Process Relationship Mapping	p. 42
3.6.3 Molecular Spatial Relationship Mapping	p. 43
3.7 Introduction to Multilevel Modeling and Data Source Integration	p. 45
3.8 Multilevel Concepts and EER Modeling	p. 46
3.9 Conclusion	p. 48
References	p. 49
Chapter 4 Fundamentals of Gene Ontology	p. 51
4.1 Introduction to Gene Ontology	p. 51
4.2 Construction of an Ontology	p. 52
4.3 General Evolution of GO Structures and General Annotation Strategy of Assigning GO Terms to Genes	p. 56
4.3.1 General Evolution of GO Structures	p. 56
4.3.2 General Annotation Strategy of Assigning GO Terms to Genes	p. 57
4.4 Applications of Gene Ontology in Biological and Medical Science	p. 57
4.4.1 Application of Gene Ontology in Biological Science	p. 57
4.4.2 Application of Gene Ontology in Medical Science	p. 58
References	p. 60
Chapter 5 Protein Ontology	p. 63
5.1 Introduction	p. 63
5.2 What Is Protein Annotation?	p. 64
5.3 Underlying Issues with Protein Annotation	p. 64
5.3.1 Other Biomedical Ontologies	p. 65
5.3.2 Protein Data Frameworks	p. 66
5.3.3 Critical Analysis of Protein Data Frameworks	p. 68
5.4 Developing Protein Ontology	p. 68
5.5 Protein Ontology Framework	p. 69
5.5.1 The ProteinOntology Concept	p. 70
5.5.2 Generic Concepts in Protein Ontology	p. 70
5.5.3 The ProteinComplex Concept	p. 71
5.5.4 Entry Concept	p. 71
5.5.5 Structure Concept	p. 72
5.5.6 StructuralDomains Concept	p. 72
5.5.7 FunctionalDomains Concept	p. 73
5.5.8 ChemicalBonds Concept	p. 74
5.5.9 Constraints Concept	p. 74
5.5.10 Comparison with Protein Annotation Frameworks	p. 75
5.6 Protein Ontology Instance Store	p. 76
5.7 Strengths and Limitations of Protein Ontology	p. 77
5.8 Summary	p. 78
References	p. 78
Chapter 6 Information Quality Management Challenges for High-Throughput Data	p. 81
6.1 Motivation	p. 81
6.2 The Experimental Context	p. 84
6.2.1 Transcriptomics	p. 86
6.2.2 Qualitative Proteomics	p. 88
6.3 A Survey of Quality Issues	p. 89
6.3.1 Variability and Experimental Design	p. 89
6.3.2 Analysis of Quality Issues and Techniques	p. 91
6.3.3 Specificity of Techniques and Generality of Dimensions	p. 93
6.3.4 Beyond Data Generation: Annotation and Presentation	p. 94
6.4 Current Approaches to Quality	p. 96
6.4.1 Modeling, Collection, and Use of Provenance Metadata	p. 96
6.4.2 Creating Controlled Vocabularies and Ontologies	p. 97
6.5 Conclusions	p. 98
Acknowledgments	p. 98
References	p. 98
Chapter 7 Data Management for Fungal Genomics: An Experience Report	p. 103
7.1 Introduction	p. 103
7.2 Materials Tracking Database	p. 109
7.3 Annotation Database	p. 110
7.4 Microarray Database	p. 111
7.5 Target Curation Database	p. 111
7.6 Discussion	p. 112
7.6.1 Issue of Data and Metadata Capture	p. 113
7.7 Conclusion	p. 116
Acknowledgments	p. 116
References	p. 116
Chapter 8 Microarray Data Management: An Enterprise Information Approach	p. 119
8.1 Introduction	p. 119
8.2 Microarray Data Standardization	p. 122
8.2.1 Gene Ontologies	p. 123
8.2.2 Microarray Ontologies	p. 125
8.2.3 Minimum Information About a Microarray Experiment	p. 125
8.3 Database Management Systems	p. 126
8.3.1 Relational Data Model	p. 127
8.3.2 Object-Oriented Data Model	p. 128
8.3.3 Object-Relational Data Model	p. 131
8.4 Microarray Data Storage and Exchange	p. 131
8.4.1 Microarray Repository	p. 133
8.4.2 Microarray Data Warehouses and Datamarts	p. 133
8.4.3 Microarray Data Federations	p. 134
8.4.4 Enterprise Microarray Databases and M-KM	p. 135
8.5 Challenges and Considerations	p. 136
8.6 Conclusions	p. 138
Acknowledgments	p. 138
References	p. 139
Chapter 9 Data Management in Expression-Based Proteomics	p. 143
9.1 Background	p. 143
9.2 Proteomics Data Management Approaches	p. 147
9.3 Data Standards in Mass Spectrometry Based Proteomics Studies	p. 149
9.4 Public Repositories for Mass Spectrometry Data	p. 152
9.5 Proteomics Data Management Tools	p. 154
9.6 Expression Proteomics in the Context of Systems Biology Studies	p. 155
9.7 Protein Annotation Databases	p. 159
9.8 Conclusions	p. 159
References	p. 160
Chapter 10 Model-Driven Drug Discovery: Principles and Practices	p. 163
10.1 Introduction	p. 163
10.2 Model Abstraction	p. 165
10.2.1 Evolution of Models	p. 166
10.3 Target Identification	p. 168
10.3.1 Sequence-to-Function Models	p. 170
10.3.2 Sequence Alignments and Phylogenetic Trees	p. 170
10.3.3 Structure-to-Function Models	p. 172
10.3.4 Systems-Based Approaches	p. 173
10.3.5 Target Validation	p. 176
10.4 Lead Identification	p. 177
10.4.1 Target Structure-Based Design	p. 177
10.4.2 Ligand-Based Models	p. 179
10.5 Lead to Drug Phase	p. 182
10.5.1 Predicting Drug-Likeness	p. 182
10.5.2 ADMET Properties	p. 182
10.6 Future Perspectives	p. 183
Acknowledgments	p. 184
References	p. 184
Chapter 11 Information Management and Interaction in High-Throughput Screening for Drug Discovery	p. 189
11.1 Introduction	p. 189
11.2 Prior Research	p. 191
11.3 Overview of Antimalarial Drug Discovery	p. 192
11.4 Overview of the Proposed Solution and System Architecture	p. 193
11.5 HTS Data Processing	p. 194
11.5.1 Introduction to HTS	p. 194
11.5.2 Example of HTS for Antimalarial Drug Screening	p. 195
11.6 Data Modeling	p. 199
11.6.1 The Database Design	p. 202
11.7 User Interface	p. 204
11.8 Conclusions	p. 206
Acknowledgments	p. 207
References	p. 207
Selected Bibliography	p. 208
About the Authors	p. 209
Index	p. 217

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