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新书资源(2012年4月)

Knowledge-based bioinformatics : from analysis to interpretation / edited by Gil Alterovitz, Marco Ramoni. — Chichester : Wiley, c2010. – (58.17115/K73)

Contents

    Contents
    
    Preface
    List of Contributors
    PART I FUNDAMENTALS
    Section 1 Knowledge-Driven Approaches
    1 Knowledge-based bioinformatics
    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
    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
    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
    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
    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
    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 131
    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
    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
    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
    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
    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
    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
    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
    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?
    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