Knowledge-Based Bioinformatics From Analysis

In order to deal with issues that arise from the current increase of biological data in genomic and proteomic research and present it effectively to a wider audience, broader coverage of recent developments in the field of knowledge-based systems and their applications is required. Most current texts are either outdated or do not include all the aspects in knowledge and data-driven representation, integration, analysis, and interpretation. This collection aims to address this issue by providing comprehensive coverage of knowledge driven approaches to bioinformatics.

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
Fundamentals
Knowledge-Driven Approaches
Knowledge-based bioinformatics
Introduction
Formal reasoning for bioinformatics
Knowledge representations
Collecting explicit knowledge
Representing common knowledge
Capturing novel knowledge
Knowledge discovery applications
Semantic harmonization: the power and limitation of ontologies
Text mining and extraction
Gene expression
Pathways and mechanistic knowledge
Genotypes and phenotypes
The Web's role in knowledge mining
New frontiers
References
Knowledge-driven approaches to genome-scale analysis
Fundamentals
Challenges in knowledge-driven approaches
Current knowledge-based bioinformatics tools
3R systems: reading, reasoning and reporting the way towards biomedical discovery
The Hanalyzer: a proof of 3R concept
Acknowledgements
References
Technologies and best practices for building bio-ontologies
Introduction
Knowledge representation languages and tools for building bio-ontologies
Best practices for building bio-ontologies
Conclusion
Acknowledgements
References
Design, implementation and updating of knowledge bases
Introduction
Sources of data in bioinformatics knowledge bases
Design of knowledge bases
Implementation of knowledge bases
Updating of knowledge bases
Conclusions
References
Data-Analysis Approaches
Classical statistical learning in bioinformatics
Introduction
Significance testing
Exploratory analysis
Classification and prediction
References
Bayesian methods in genomics and proteomics studies
Introduction
Bayes theorem and some simple applications
Inference of population structure from genetic marker data
Inference of protein binding motifs from sequence data
Inference of transcriptional regulatory networks from joint analysis of protein-DNA binding data and gene expression data
Inference of protein and domain interactions from yeast two-hybrid data
Conclusions
Acknowledgements
References
Automatic text analysis for bioinformatics knowledge discovery
Introduction
Information needs for biomedical text mining
Principles of text mining
Development issues
Success stories
Conclusion
References
Applications
Gene and Protein Information
Fundamentals of gene ontology functional annotation
Introduction
Gene Ontology (GO)
Comparative genomics and electronic protein annotation
Community annotation
Limitations
Accessing GO annotations
Conclusions
References
Methods for improving genome annotation
The basis of gene annotation
The impact of next generation sequencing on genome annotation
References
Sequences from prokaryotic, eukaryotic, and viral genomes available clustered according to phylotype on a Self-Organizing Map
Introduction
Batch-learning SOM (BLSOM) adapted for genome informatics
Genome sequence analyses using BLSOM
Conclusions and discussion
References
Biomolecular Relationships and Meta-Relationships
Molecular network analysis and applications
Introduction
Topology analysis and applications
Network motif analysis
Network modular analysis and applications
Network comparison
Network analysis software and tools
Summary
Acknowledgement
References
Biological pathway analysis: an overview of Reactome and other integrative pathway knowledge bases
Biological pathway analysis and pathway knowledge bases
Overview of high-throughput data capture technologies and data repositories
Brief review of selected pathway knowledge bases
How does information get into pathway knowledge bases?
Introduction to data exchange languages
Visualization tools
Use case: pathway analysis in Reactome using statistical analysis of high-throughput data sets
Discussion: challenges and future directions of pathway knowledge bases
References
Methods and challenges of identifying biomolecular relationships and networks associated with complex diseases/phenotypes, and their application to drug treatments
Complex traits: clinical phenomenology and molecular background
Why it is challenging to infer relationships between genes and phenotypes in complex traits? 317
Bottom-up or top-down: which approach is more useful in delineating complex traits key drivers?
High-throughput technologies and their applications in complex traits genetics
Integrative systems biology: a comprehensive approach to mining high-throughput data
Methods applying systems biology approach in the identification of functional relationships from gene expression data
Advantages of networks exploration in molecular biology and drug discovery
Practical examples of applying systems biology approaches and network exploration in the identification of functional modules and disease-causing genes in complex phenotypes/diseases
Challenges and future directions
References
Trends and conclusion
Index
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Knowledge-Based Bioinformatics From Analysis to Interpretation

Summary

Author Biography

Table of Contents

Supplemental Materials