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 to help bioinformatics professionals develop ever-more powerful data management systems in any domain. Breaking new ground at the intersection of life sciences and data management, the book introduces practitioners to core biodata modeling techniques, biological database resources, and ontology concepts. It explains the latest envelope-pushing methods and software applications for processing, integrating, and managing biodata.
Author(s): Jake Chen, Amandeep S. Sidhu
Edition: 1
Publisher: Artech House
Year: 2008
Language: English
Pages: 242
City: Norwood, MA
Tags: Биологические дисциплины;Матметоды и моделирование в биологии;Биоинформатика;
Biological Database Modeling......Page 1
Contents......Page 7
Preface......Page 13
Acknowledgments......Page 17
1.1 Generic Modern Markup Languages......Page 19
1.3 Data Modeling with General Markup Languages......Page 21
1.4 Ontologies: Enriching Data with Text......Page 22
1.5 Hyperlinks for Semantic Modeling......Page 23
1.7 Languages......Page 24
1.9 Modeling Biological Data......Page 25
2.1 Introduction......Page 27
2.2 Public Databases in Medicine......Page 28
2.3 Application of Public Bioinformatics Database in Medicine......Page 29
3.1 Introduction......Page 43
3.2 Biological Concepts and EER Modeling......Page 45
3.3 Formal Definitions for EER Extensions......Page 49
3.5 Semantic Data Models of the Molecular Biological System......Page 53
3.6 EER-to-Relational Mapping......Page 59
3.7 Introduction to Multilevel Modeling and Data Source Integration......Page 63
3.8 Multilevel Concepts and EER Modeling......Page 64
3.9 Conclusion......Page 66
4.1 Introduction to Gene Ontology......Page 69
4.2 Construction of an Ontology......Page 70
4.3 General Evolution of GO Structures and General Annotation Strategy ofAssigning GO Terms to Genes......Page 74
4.4 Applications of Gene Ontology in Biological and Medical Science......Page 75
5.1 Introduction......Page 81
5.3 Underlying Issues with Protein Annotation......Page 82
5.4 Developing Protein Ontology......Page 86
5.5 Protein Ontology Framework......Page 87
5.6 Protein Ontology Instance Store......Page 94
5.7 Strengths and Limitations of Protein Ontology......Page 95
5.8 Summary......Page 96
6.1 Motivation......Page 99
6.2 The Experimental Context......Page 102
6.3 A Survey of Quality Issues......Page 107
6.4 Current Approaches to Quality......Page 114
6.5 Conclusions......Page 116
7.1 Introduction......Page 121
7.2 Materials Tracking Database......Page 127
7.3 Annotation Database......Page 128
7.5 Target Curation Database......Page 129
7.6 Discussion......Page 130
7.7 Conclusion......Page 134
8.1 Introduction......Page 137
8.2 Microarray Data Standardization......Page 140
8.3 Database Management Systems......Page 144
8.4 Microarray Data Storage and Exchange......Page 149
8.5 Challenges and Conside......Page 154
8.6 Conclusions......Page 156
9.1 Background......Page 161
9.2 Proteomics Data Management Approaches......Page 165
9.3 Data Standards in Mass Spectrometry Based Proteomics Studies......Page 167
9.4 Public Repositories for Mass Spectrometry Data......Page 170
9.5 Proteomics Data Management Tools......Page 172
9.6 Expression Proteomics in the Context of Systems Biology Studies......Page 173
9.8 Conclusions......Page 177
10.1 Introduction......Page 181
10.2 Model Abstraction......Page 183
10.3 Target Identification......Page 186
10.4 Lead Identification......Page 195
10.5 Lead to Drug Phase......Page 200
10.6 Future Perspectives......Page 201
11.1 Introduction......Page 207
11.2 Prior Research......Page 209
11.3 Overview of Antimalarial Drug Discovery......Page 210
11.4 Overview of the Proposed Solution and System Architecture......Page 211
11.5 HTS Data Processing......Page 212
11.6 Data Modeling......Page 217
11.7 User Interface......Page 222
11.8 Conclusions......Page 224
About the Authors......Page 227
Index......Page 235
