Preface.
List of Contributors.
PART I FUNDAMENTALS.
Section 1 Knowledge-Driven Approaches.
1 Knowledge-based bioinformatics (Eric Karl
Neumann).
1.1 Introduction.
1.2 Formal reasoning for bioinformatics.
1.3 Knowledge representations.
1.4 Collecting explicit knowledge.
1.5 Representing common knowledge.
1.6 Capturing novel knowledge.
1.7 Knowledge discovery applications.
1.8 Semantic harmonization: the power and limitation of
ontologies.
1.9 Text mining and extraction.
1.10 Gene expression.
1.11 Pathways and mechanistic knowledge.
1.12 Genotypes and phenotypes.
1.13 The Web's role in knowledge mining.
1.14 New frontiers.
1.15 References.
2 Knowledge-driven approaches to genome-scale
analysis (Hannah Tipney and Lawrence Hunter).
2.1 Fundamentals.
2.2 Challenges in knowledge-driven approaches.
2.3 Current knowledge-based bioinformatics tools.
2.4 3R systems: reading, reasoning and reporting the way towards
biomedical discovery.
2.5 The Hanalyzer: a proof of 3R concept.
2.6 Acknowledgements.
2.7 References.
3 Technologies and best practices for building
bio-ontologies (Mikel Egana Aranguren, Robert
Stevens, Erick Antezana, Jesualdo Tomas Fernandez-Breis,
Martin Kuiper, and Vladimir Mironov).
3.1 Introduction.
3.2 Knowledge representation languages and tools for building
bio-ontologies.
3.3 Best practices for building bio-ontologies.
3.4 Conclusion.
3.5 Acknowledgements.
3.6 References.
4 Design, implementation and updating of knowledge
bases (Sarah Hunter, Rolf Apweiler, and Maria Jesus
Martin).
4.1 Introduction.
4.2 Sources of data in bioinformatics knowledge bases.
4.3 Design of knowledge bases.
4.4 Implementation of knowledge bases.
4.5 Updating of knowledge bases.
4.6 Conclusions.
4.7 References.
Section 2 Data-Analysis Approaches.
5 Classical statistical learning in
bioinformatics (Mark Reimers).
5.1 Introduction.
5.2 Significance testing.
5.3 Exploratory analysis.
5.4 Classification and prediction.
5.5 References.
6 Bayesian methods in genomics and proteomics
studies (Ning Sun and Hongyu Zhao).
6.1 Introduction.
6.2 Bayes theorem and some simple applications.
6.3 Inference of population structure from genetic marker
data.
6.4 Inference of protein binding motifs from sequence data.
6.5 Inference of transcriptional regulatory networks from joint
analysis of protein?DNA binding data and gene expression
data.
6.6 Inference of protein and domain interactions from yeast
two-hybrid data.
6.7 Conclusions.
6.8 Acknowledgements.
6.9 References.
7 Automatic text analysis for bioinformatics knowledge
discovery (Dietrich Rebholz-Schuhmann and Jung-jae
Kim).
7.1 Introduction.
7.2 Information needs for biomedical text mining.
7.3 Principles of text mining.
7.4 Development issues.
7.5 Success stories.
7.6 Conclusion.
7.7 References.
PART II APPLICATIONS.
Section 3 Gene and Protein Information.
8 Fundamentals of gene ontology functional
annotation (Varsha K. Khodiyar, Emily C. Dimmer,
Rachael P. Huntley, and Ruth C. Lovering).
8.1 Introduction.
8.2 Gene Ontology (GO).
8.3 Comparative genomics and electronic protein annotation.
8.4 Community annotation.
8.5 Limitations.
8.6 Accessing GO annotations.
8.7 Conclusions.
8.8 References.
9 Methods for improving genome
annotation (Jonathan Mudge and Jennifer
Harrow).
9.1 The basis of gene annotation.
9.2 The impact of next generation sequencing on genome
annotation.
9.3 References.
10 Sequences from prokaryotic, eukaryotic, and viral genomes
available clustered according to phylotype on a Self-Organizing
Map (Takashi Abe, Shigehiko Kanaya, and Toshimichi
Ikemura).
10.1 Introduction.
10.2 Batch-learning SOM (BLSOM) adapted for genome
informatics.
10.3 Genome sequence analyses using BLSOM.
10.4 Conclusions and discussion.
10.5 References.
Section 4 Biomolecular Relationships and
Meta-Relationships.
11 Molecular network analysis and
applications (Minlu Zhang, Jingyuan Deng, Chunsheng V.
Fang, Xiao Zhang, and Long Jason Lu).
11.1 Introduction.
11.2 Topology analysis and applications.
11.3 Network motif analysis.
11.4 Network modular analysis and applications.
11.5 Network comparison.
11.6 Network analysis software and tools.
11.7 Summary.
11.8 Acknowledgement.
11.9 References.
12 Biological pathway analysis: an overview of Reactome and
other integrative pathway knowledge bases (Robin A.
Haw, Marc E. Gillespie, and Michael A. Caudy).
12.1 Biological pathway analysis and pathway knowledge
bases.
12.2 Overview of high-throughput data capture technologies and
data repositories.
12.3 Brief review of selected pathway knowledge bases.
12.4 How does information get into pathway knowledge bases?
12.5 Introduction to data exchange languages.
12.6 Visualization tools.
12.7 Use case: pathway analysis in Reactome using statistical
analysis of high-throughput data sets.
12.8 Discussion: challenges and future directions of pathway
knowledge bases.
12.9 References.
13 Methods and challenges of identifying biomolecular
relationships and networks associated with complex
diseases/phenotypes, and their application to drug
treatments (Mie Rizig).
13.1 Complex traits: clinical phenomenology and molecular
background.
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?
13.4 High-throughput technologies and their applications in
complex traits genetics.
13.5 Integrative systems biology: a comprehensive approach to
mining high-throughput data.
13.6 Methods applying systems biology approach in the
identification of functional relationships from gene expression
data.
13.7 Advantages of networks exploration in molecular biology and
drug discovery.
13.8 Practical examples of applying systems biology approaches
and network exploration in the identification of functional modules
and disease-causing genes in complex phenotypes/diseases.
13.9 Challenges and future directions.
13.10 References.
Trends and conclusion.
Index.
