Knowledge-Based Bioinformatics: From analysis to interpretation [NOOK Book]

Overview

There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough ...

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Knowledge-Based Bioinformatics: From analysis to interpretation

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Overview

There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine.

Key Features:

  • Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology.
  • Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions.
  • Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics.
  • Written by leading international experts in this field.

Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.

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Product Details

  • ISBN-13: 9781119995838
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 4/20/2011
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 396
  • Product dimensions: 6.44 (w) x 9.19 (h) x 1.05 (d)
  • File size: 11 MB
  • Note: This product may take a few minutes to download.

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Table of Contents

Preface

List of Contributors

PART I FUNDAMENTALS 1

Section 1 Knowledge-Driven Approaches 3

1 Knowledge-based bioinformatics Eric Karl Neumann Neumann, Eric Karl 5

1.1 Introduction 5

1.2 Formal reasoning for bioinformatics 7

1.3 Knowledge representations 10

1.4 Collecting explicit knowledge 10

1.5 Representing common knowledge 11

1.6 Capturing novel knowledge 15

1.7 Knowledge discovery applications 15

1.8 Semantic barmonization: the power and limitation of ontologies 18

1.9 Text mining and extraction 19

1.10 Gene expression 20

1.11 Pathways and mechanistic knowledge 22

1.12 Genotypes and phenotypes 24

1.13 The Web's role in knowledge mining 25

1.14 New frontiers 26

1.14.1 Requirements for linked knowledge discovery 26

1.14.2 Information aggregation 26

1.14.3 The Linked Open Data initiative 28

1.14.4 Information articulation 28

1.14.5 Next-generation knowledge discovery 30

1.15 References 31

2 Knowledge-driven approaches to genome-scale analysis Lawrence Hunter Hunter, Lawrence 33

2.1 Fundamentals 33

2.1.1 The genomic era and systems biology 33

2.1.2 The exponential growth of biomedical knowledge 34

2.1.3 The challenges of finding and interacting with biomedical knowledge 35

2.2 Challenges in knowledge-driven approaches 37

2.2.1 We need to read; development of automatic methods to extract data housed in the biomedical literature 37

2.2.2 Implicit and implied knowledge; the forgotten data source 41

2.2.3 Humans are visual beings: so should their knowledge be 42

2.3 Current Knowledge-based bioinformatics tools 43

2.3.1 Enrichment tools 44

2.3.2 Integration and expansion: from gene lists to networks 46

2.3.3 Expanding the concept of an interaction 48

2.3.4 A systematic failure to support advanced scientific reasoning 50

2.4 3R systems: reading, reasoning and reporting the way towards biomedical discovery 50

2.4.1 3R knowledge networks populated by reading and reasoning 52

2.4.2 Implied association results in uncertainty 53

2.4.3 Reporting: using 3R knowledge networks to tell biological stories 54

2.5 The Hanalyzer: a proof of 3R concept 55

2.6 Acknowledgements 62

2.7 References 62

3 Technologies and best practices for building bio-ontologies Vladimir Mironov Mironov, Vladimir 67

3.1 Introduction 67

3.2 Knowledge representation languages and tools for building bio-ontologies 68

3.2.1 RDF (resource description framework) 71

3.2.2 OWL (Web ontology language) 72

3.2.3 OBO format 77

3.3 Best practices for building bio-ontologies 78

3.3.1 Define the scope of the bio-ontology 78

3.3.2 Identity of the represented entities 79

3.3.3 Commit to agreed ontological principles 79

3.3.4 Knowledge acquisition 80

3.3.5 Ontology Design Patterns (ODPs) 80

3.3.6 Ontology evaluation 81

3.3.7 Documentation 83

3.4 Conclusion 83

3.5 Acknowledgements 84

3.6 References 84

4 Design, implementation and updating of knowledge bases Maria Jesus Martin Martin, Maria Jesus 87

4.1 Introduction 87

4.2 Sources of data in bioinformatics knowledge bases 90

4.2.1 Data added by internal curators 90

4.2.2 Data submitted by external users and collaborators 90

4.2.3 Data added automatically 91

4.3 Design of knowledge bases 91

4.3.1 Understanding your end users and understanding their data 92

4.3.2 Interactions and interfaces: their impact on design 93

4.4 Implementation of knowledge bases 93

4.4.1 Choosing a database architecture 93

4.4.2 Good programming practices 96

4.4.3 Implementation of interfaces 97

4.5 Updating of knowledge bases 98

4.5.1 Manual curation and auto-annotation 98

4.5.2 Clever pipelines and data flows 101

4.5.3 Lessening data maintenance overheads 104

4.6 Conclusions 105

4.7 References 105

Section 2 Data-Analysis Approaches 107

5 Classical statistical learning in bioinformatics Mark Reimers Reimers, Mark 109

5.1 Introduction 109

5.2 Significance testing 109

5.2.1 Multiple testing and false discovery rate 110

5.2.2 Correlated errors 111

5.3 Exploratory analysis 112

5.3.1 Clustering 112

5.3.2 Principal components 116

5.3.3 Multidimensional scaling (MDS) 117

5.4 Classification and prediction 119

5.4.1 Discriminant analysis 120

5.4.2 Modern procedures 120

5.5 References 122

6 Bayesian methods in genomics and proteomics studies Hongyu Zhao Zhao, Hongyu 125

6.1 Introduction 125

6.2 Bayes theorem and some simple applications 126

6.3 Inference of population structure from genetic marker data 129

6.4 Inference of protein binding motifs from sequence data 130

6.5 Inference of transcriptional regulatory networks from joint analysis of protein-DNA binding data and gene expression data 131

6.6 Inference of protein and domain interactions from yeast two-hybrid data 132

6.7 Conclusions 134

6.8 Acknowledgements 135

6.9 References 135

7 Automatic text analysis for bioinformatics knowledge discovery Jung-Jae Kim Kim, Jung-Jae 137

7.1 Introduction 137

7.1.1 Knowledge discovery through text mining 138

7.1.2 Need for processing biomedical texts 139

7.1.3 Developing text mining solutions 141

7.2 Information needs for biomedical text mining 142

7.2.1 Efficient analysis of normalized information 142

7.2.2 Interactive seeking of textual information 145

7.3 Principles of text mining 147

7.3.1 Components 147

7.3.2 Methods 150

7.4 Development issues 152

7.4.1 Information needs 153

7.4.2 Corpus construction 153

7.4.3 Language analysis 154

7.4.4 Integration framework 154

7.4.5 Evaluation 155

7.5 Success stories 156

7.5.1 Interactive literature analysis 156

7.5.2 Integration into bioinformatics solutions 157

7.5.3 Discovery of knowledge from the literature 158

7.6 Conclusion 159

7.7 References 160

PART II APPLICATIONS 169

Section 3 Gene and Protein Information 171

8 Fundamentals of gene ontology functional annotation Ruth C. Lovering Lovering, Ruth C. 173

8.1 Introduction 173

8.1.1 Data submission curation 174

8.1.2 Value-added curation 174

8.2 Gene Ontology (GO) 175

8.2.1 Gene Ontology and the annotation of the human proteome 175

8.2.2 Gene Ontology Consortium data sets 176

8.2.3 GO annotation methods 176

8.2.4 Different approaches to manual annotation 183

8.2.5 Ontology development 183

8.3 Comparative genomics and electronic protein annotation 186

8.3.1 Manual methods of transferring functional annotation 186

8.3.2 Electronic methods of transferring functional annotation 187

8.3.3 Electronic annotation methods 188

8.4 Community annotation 189

8.4.1 Feedback forms 190

8.4.2 Wiki pages 190

8.4.3 Community annotation workshops 190

8.5 Limitations 191

8.5.1 Go cannot capture all relevant biological aspects 191

8.5.2 The ontology is always evolving 192

8.5.3 The volume of literature 192

8.5.4 Missing published data 192

8.5.5 Manual curation is expensive 192

8.6 Accessing GO annotations 193

8.6.1 Tools for browsing the GO 194

8.6.2 Functional classification 199

8.6.3 GO slims 202

8.6.4 GO displays in other databases 203

8.7 Conclusions 203

8.8 References 204

9 Methods for improving genome annotation Jennifer Harrow Harrow, Jennifer 209

9.1 The basis of gene annotation 209

9.1.1 Introduction to gene annotation 209

9.1.2 Progression in ab initio gene prediction 211

9.1.3 Annotation based on transcribed evidence 211

9.1.4 A comparison of annotation processes 213

9.1.5 The CCDS project 214

9.1.6 Pseudogene annotation 215

9.1.7 The annotation of non-coding genes 218

9.2 The impact of next generation sequencing on genome annotation 220

9.2.1 The annotation of multispecies genomes 220

9.2.2 Community annotation 222

9.2.3 Alternative splicing and new transcriptomics data 223

9.2.4 The annotation of human genome variation 225

9.2.5 The annotation of polymorphic gene families 226

9.3 References 228

10 Sequences from prokaryotic, eukaryotic, and viral genomes available clustered according to phylotype on a Self-Organizing Map Toshimichi Ikemura Ikemura, Toshimichi 233

10.1 Introduction 233

10.2 Batch-learning SOM (BLSOM) adapted for genome informatics 235

10.3 Genome sequence analyses using BLSOM 237

10.3.1 BLSOMs for 13 eukaryotic genomes 237

10.3.2 Diagnostic oligonucleotides for phylotype-specific clustering 238

10.3.3 A large-scale BLSOM constructed with all sequences available from species-known genomes 240

10.3.4 Phylogenetic estimation for environmental DNA sequences and microbial community comparison using the BLSOM 242

10.3.5 Reassociation of environmental genomic fragments according to species 245

10.4 Conclusions and discussion 247

10.5 References 248

Section 4 Biomolecular Relationships and Meta-Relationships 251

11 Molecular network analysis and applications Long Jason Lu Long, Jason Lu 253

11.1 Introduction 253

11.2 Topology analysis and applications 254

11.2.1 Global structure of molecular networks: scale-free, small-world, disassortative, and modular 254

11.2.2 Network statistics/measures 258

11.2.3 Applications of topology analysis 258

11.2.4 Challenges and future directions of topology analysis 262

11.3 Network motif analysis 263

11.3.1 Motif analysis: concept and method 263

11.3.2 Applications of motif analysis 263

11.3.3 Challenges and future directions of motif analysis 266

11.4 Network modular analysis and applications 267

11.4.1 Density-based clustering methods 268

11.4.2 Partition-based clustering methods 269

11.4.3 Centrality-based clustering methods 270

11.4.4 Hierarchical clustering methods 271

11.4.5 Applications of modular analysis 272

11.4.6 Challenges and future directions of modular analysis 273

11.5 Network comparison 274

11.5.1 Network comparison algorithms: from computer science to systems biology 274

11.5.2 Network comparison algorithms for molecular networks 275

11.5.3 Applications of molecular network comparison 277

11.5.4 Challenges and future directions of network comparison 278

11.6 Network analysis software and tools 279

11.7 Summary 279

11.8 Acknowledgement 282

11.9 References 282

12 Biological pathway analysis: an overview of Reactome and other intergrative pathway knowledge bases Michael A. Caudy Caudy, Michael A. 289

12.1 Biological pathway analysis and pathway knowledge bases 289

12.2 Overview of high-throughput data capture technologies and data repositories 290

12.3 Brief review of selected pathway knowledge bases 293

12.3.1 Reactome 293

12.3.2 KEGG 296

12.3.3 WikiPathways 297

12.3.4 NCI-Pathway Interaction Database 297

12.3.5 NCBI-BioSystems 298

12.3.6 Science Signaling 299

12.3.7 PharmGKB 299

12.4 How does information get into pathway knowledge bases? 300

12.5 Introduction to data exchange languages 301

12.5.1 SBML 301

12.5.2 BioPAX 302

12.5.3 PSI MI 303

12.5.4 Comparison of data exchange formats for different pathway knowledge bases 303

12.6 Visualization tools 304

12.7 Use case: pathway analysis in Reactome using statistical analysis of high-throughput data sets 305

12.8 Discussion: challenges and future directions of pathway knowledge bases 310

12.9 References 311

13 Methods and challenges of identifying biomolecular relationships and networks associated with complex diseases/phenotypes, and their application to drug treatments Mie Rizig Mie, Rizig 315

13.1 Complex traits: clinical phenomenology and molecular background 315

13.2 Why it is challenging to infer relationships between genes and phenotypes in complex traits? 317

13.3 Bottom-up or top-down: which approach is more useful in delineating complex traits key drivers? 325

13.4 High-throughput technologies and their applications in complex traits genetics 327

13.5 Integrative systems biology: a comprehensive approach to mining high-throughput data 328

13.6 Methods applying systems biology approach in the identification of functional relationships from gene expression data 331

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