Microarrays for an integrative genomics

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Microarrays for an integrative genomics / Isaac S. Kohane, Alvin T. Kho, and Atul J. Butte

資料種別:

図書

出版情報:

Cambridge, Mass. ; London : MIT Press, c2003

形態:

xviii, 306 p ; 24 cm

シリーズ名:

Computational molecular biology <BA48845493>

著者名:

ISBN:

9780262112710 [026211271X]

書誌ID:

BA59288934

子書誌情報

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書誌詳細

注記:

Includes bibliographical references and index

主題:

Bioinformatics; DNA microarrays; Genomics--Automation

言語:

英語

目次情報:

Foreword

Preface

Acknowledgments

Introduction / 1：

The Future Is So Bright... / 1.1：

Functional Genomics / 1.2：

Informatics and advances in enabling technology / 1.2.1：

Why do we need new techniques? / 1.2.2：

Missing the Forest for the Dendrograms / 1.3：

Sociology of a functional genomics pipeline / 1.3.1：

Functional Genomics, Not Genetics / 1.4：

In silico analysis will never substitute for in vitro and in vivo / 1.4.1：

Basic Biology / 1.5：

Biological caveats in mRNA measurements / 1.5.1：

Sequence-level genomics / 1.5.2：

Proteomics / 1.5.3：

Experimental Design / 2：

The Safe Conception of a Functional Genomic Experiment / 2.1：

Experiment design space / 2.1.1：

Expression space / 2.1.2：

Exercising the expression space / 2.1.3：

Discarding data and low-hanging fruit / 2.1.4：

Gene-Clustering Dogma / 2.2：

Supervised versus unsupervised learning / 2.2.1：

Figure of merit: The elusive gold standard in functional genomics / 2.2.2：

Microarray Measurements to Analyses / 3：

Generic Features of Microarray Technologies / 3.1：

Robotically spotted microarrays / 3.1.1：

Oligonucleotide microarrays / 3.1.2：

Replicate Experiments, Reproducibility, and Noise / 3.2：

What is a replicate experiment? A reproducible experimental outcome? / 3.2.1：

Reproducibility across repeated microarray experiments: Absolute expression level and fold difference / 3.2.2：

Cross-platform (technology) reproducibility / 3.2.3：

Pooling sample probes and PCR for replicate experiments / 3.2.4：

What is noise? / 3.2.5：

Sources and examples of noise in the generic microarray experiment / 3.2.6：

Biological variation as noise: The Human Genome Project and irreproducibility of expression measurements / 3.2.7：

Managing noise / 3.2.8：

Prototypical Objectives and Questions / 3.3：

Two examples: Inter-array and intra-array / 3.3.1：

Preprocessing: Filters and Normalization / 3.4：

Normalization / 3.4.1：

Background on Fold / 3.5：

Fold calculation and significance / 3.5.1：

Fold change may not mean the same thing in different expression measurement technologies / 3.5.2：

Dissimilarity and Similarity Measures / 3.6：

Linear correlation / 3.6.1：

Entropy and mutual information / 3.6.2：

Dynamics / 3.6.3：

Genomic Data-Mining Techniques / 4：

What Can Be Clustered in Functional Genomics? / 4.1：

What Does it Mean to Cluster? / 4.3：

Hierarchy of Bioinformatics Algorithms / 4.4：

Data Reduction and Filtering / 4.5：

Variation filter / 4.5.1：

Low entropy filter / 4.5.2：

Minimum expression level filter / 4.5.3：

Target ambiguity filter / 4.5.4：

Self-Organizing Maps / 4.6：

K-means clustering / 4.6.1：

Finding Genes That Split Sets / 4.7：

Phylogenetic-Type Trees / 4.8：

Two-dimensional dendrograms / 4.8.1：

Relevance Networks / 4.9：

Other Methods / 4.10：

Which Technique Should I Use? / 4.11：

Determining the Significance of Findings / 4.12：

Permutation testing / 4.12.1：

Testing and training sets / 4.12.2：

Performance metrics / 4.12.3：

Receiver operating characteristic curves / 4.12.4：

Genetic Networks / 4.13：

What is a genetic network? / 4.13.1：

Reverse-engineering and modeling a genetic network using limited data / 4.13.2：

Bayesian networks for functional genomics / 4.13.3：

Bio-Ontologies, Data Models, Nomenclature / 5：

Ontologies / 5.1：

Bio-ontology projects / 5.1.1：

Advanced knowledge representation systems for bio-ontology / 5.1.2：

Expressivity versus Computability / 5.2：

Ontology versus Data Model versus Nomenclature / 5.3：

Exploiting the explicit and implicit ontologies of the biomedical literature / 5.3.1：

Data Model Introduction / 5.4：

Nomenclature / 5.5：

The unique gene identifier / 5.5.1：

Postanalysis Challenges / 5.6：

Linking to downstream biological validation / 5.6.1：

Problems in determining the results / 5.6.2：

From Functional Genomics to Clinical Relevance / 6：

Electronic Medical Records / 6.1：

Standardized Vocabularies for Clinical Phenotypes / 6.2：

Privacy of Clinical Data / 6.3：

Anonymization / 6.3.1：

Privacy rules / 6.3.2：

Costs of Clinical Data Acquisition / 6.4：

The Near Future / 7：

New Methods for Gene Expression Profiling / 7.1：

Electronic positioning of molecules: Nanogen / 7.1.1：

Ink-jet spotting of arrays: Agilent / 7.1.2：

Coded microbeads bound to oligonucleotides: Illumina / 7.1.3：

Serial Analysis of Gene Expression (SAGE) / 7.1.4：

Parallel signature sequencing on microbead arrays: Lynx / 7.1.5：

Gel pad technology: Motorola / 7.1.6：

Respecting the Older Generation / 7.2：

The generation gap / 7.2.1：

Separating the wheat from the chaff / 7.2.2：

A persistent problem / 7.2.3：

Selecting Software / 7.3：

Investing in the Future of the Genomic Enterprise / 7.4：

Glossary

Foreword

Preface

Acknowledgments

Introduction / 1：

The Future Is So Bright... / 1.1：

Functional Genomics / 1.2：

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