Knowledge-Based Bioinformatics
Herausgeber: Alterovitz, Gil; Ramoni, Marco
Knowledge-Based Bioinformatics
Herausgeber: Alterovitz, Gil; Ramoni, Marco
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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…mehr
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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.
Produktdetails
- Produktdetails
- Verlag: John Wiley & Sons / Wiley
- Seitenzahl: 400
- Erscheinungstermin: 23. August 2010
- Englisch
- Abmessung: 235mm x 157mm x 26mm
- Gewicht: 724g
- ISBN-13: 9780470748312
- ISBN-10: 0470748311
- Artikelnr.: 29936224
- Verlag: John Wiley & Sons / Wiley
- Seitenzahl: 400
- Erscheinungstermin: 23. August 2010
- Englisch
- Abmessung: 235mm x 157mm x 26mm
- Gewicht: 724g
- ISBN-13: 9780470748312
- ISBN-10: 0470748311
- Artikelnr.: 29936224
Dr Gil Alterovitz, Harvard Medical School & Massachusetts Institute of Technology, USA Dr Alterovitz regularly lectures on Bioinformatics and biomedical computing. He is the Editor of the successful Artech House book Systems Bioinformatics (2007) Dr Marco Ramoni, Harvard Medical School & Massachusetts Institute of Technology, USA Dr Ramoni is the Associate Director of Bioinformatics at Harvard Medical school. He has written numerous papers and book chapters on bioinformatics and is regularly invited to speak at conferences.
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 Egaña Aranguren, Robert Stevens, Erick
Antezana, Jesualdo Tomás Fernández-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.
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 Egaña Aranguren, Robert Stevens, Erick
Antezana, Jesualdo Tomás Fernández-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.
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 Egaña Aranguren, Robert Stevens, Erick
Antezana, Jesualdo Tomás Fernández-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.
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 Egaña Aranguren, Robert Stevens, Erick
Antezana, Jesualdo Tomás Fernández-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.