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1.

図書
図書
1. DNA microarray technology and data analysis in cancer research
Shaoguang Li, Dongguang Li
出版情報: Hackensack, N.J. : World Scientific, c2008  xi, 118 p. ; 24 cm
目次情報: 続きを見る
Preface
Introduction of Authors
DNA Microarray Technology / Chapter 1：
Experimental Procedure / 1.1：
Experimental Design / 1.2：
Quality Control / 1.3：
Interpretation of DNA Microarray Data / 1.4：
Advantages and Disadvantages / 1.5：
Applications of DNA Microarray Technology in Cancer Research / Chapter 2：
Solid Tumors / 2.1：
Blood Cancers / 2.2：
Our DNA Microarray Study Using Mouse Model of BCR-ABL-Induced Leukemia / 2.3：
Leukemia mouse model study / 2.3.1：
Cell line study / 2.3.2：
Current Analytical Methods of DNA Microarray Data / Chapter 3：
Method / 3.1：
Robust multi-chip averaging (RMA) / 3.2.1：
iterPLIER / 3.2.2：
Quality Control Diagnostics / 3.3：
Saturation / 3.3.1：
Transformed intensities across arrays / 3.3.2：
Normalized intensities across arrays / 3.3.3：
Scatterplot of normalized intensities / 3.3.4：
Average MA plot of normalized intensities / 3.3.5：
Statistical Analysis / 3.4：
Analysis of variance (ANOVA) model / 3.4.1：
Contrasts / 3.4.2：
Coefficient of Variation Analysis / 3.5：
A Novel Method for DNA Microarray Data Analysis: SDL Global Optimization Method / Chapter 4：
Research Subjects / 4.1：
Rationale / 4.2：
Fold Change Analysis / 4.3：
More Information on SDL Global Optimization / 4.4：
Genetic algorithms (GAs) / 4.4.1：
SDL global optimization algorithms / 4.4.2：
Applications of the SDL Global Optimization Method in DNA Microarray Data Analysis / Chapter 5：
Leukemia Cell Line Study / 5.1：
Introduction / 5.1.1：
Datasets / 5.1.2：
Analysis strategies / 5.1.3：
Discussion and conclusion / 5.1.4：
Analyses of Publicly Available Human Microarray Data / 5.2：
Overall Methodology / 5.2.1：
Orthogonal arrays (OAs) and sampling procedure / 5.3.1：
Objective function / 5.3.2：
Search space reduction for global search / 5.3.3：
Mathematical form of SDL optimization / 5.3.4：
Multi-subset class predictor / 5.3.5：
Validation (predicting through a voting mechanism) / 5.3.6：
Experimental Results / 5.4：
Discussion / 5.5：
Conclusion / 5.6：
General Discussion and Future Directions / Chapter 6：
References
Index
Preface
Introduction of Authors
DNA Microarray Technology / Chapter 1：
2.

図書
図書
2. Microarrays for an integrative genomics
Isaac S. Kohane, Alvin T. Kho, and Atul J. Butte
出版情報: Cambridge, Mass. ; London : MIT Press, c2003  xviii, 306 p ; 24 cm
シリーズ名: Computational molecular biology
目次情報: 続きを見る
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
3.

図書
図書
3. Microarray image analysis : an algorithmic approach (: hard)
Karl Fraser, Zidong Wang, Xiaohui Liu
出版情報: Boca Raton, FL : Chapman & Hall/CRC, c2010  xxiv, 311 p. ; 25 cm
シリーズ名: Series in computer science and data analysis
4.

図書
図書
4. Advanced analysis of gene expression microarray data
Aidong Zhang
出版情報: Hackensack, NJ : World Scientific, c2006  xv, 339 p. ; 24 cm
目次情報: 続きを見る
Preface
Introduction / 1：
The Microarray: Key to Functional Genomics and Systems Biology / 1.1：
Applications of Microarray / 1.2：
Gene Expression Profiles in Different Tissues / 1.2.1：
Developmental Genetics / 1.2.2：
Gene Expression Patterns in Model Systems / 1.2.3：
Differential Gene Expression Patterns in Diseases / 1.2.4：
Gene Expression Patterns in Pathogens / 1.2.5：
Gene Expression in Response to Drug Treatments / 1.2.6：
Genotypic Analysis / 1.2.7：
Mutation Screening of Disease Genes / 1.2.8：
Framework of Microarray Data Analysis / 1.3：
Summary / 1.4：
Basic Concepts of Molecular Biology / 2：
Cells / 2.1：
Proteins / 2.3：
Nucleic Acids / 2.4：
DNA / 2.4.1：
RNA / 2.4.2：
Central Dogma of Molecular Biology / 2.5：
Genes and the Genetic Code / 2.5.1：
Transcription and Gene Expression / 2.5.2：
Translation and Protein Synthesis / 2.5.3：
Genotype and Phenotype / 2.6：
Overview of Microarray Experiments / 2.7：
Microarray Chip Manufacture / 3.1：
Deposition-Based Manufacture / 3.2.1：
In Situ Manufacture / 3.2.2：
The Affymetrix GeneChip / 3.2.2.1：
Steps of Microarray Experiments / 3.3：
Sample Preparation and Labeling / 3.3.1：
Hybridization / 3.3.2：
Image Scanning / 3.3.3：
Image Processing / 3.4：
Microarray Data Cleaning and Preprocessing / 3.5：
Data Transformation / 3.5.1：
Missing Value Estimation / 3.5.2：
Data Normalization / 3.6：
Global Normalization Approaches / 3.6.1：
Standardization / 3.6.1.1：
Iterative linear regression / 3.6.1.2：
Intensity-Dependent Normalization / 3.6.2：
LOWESS: Locally weighted linear regression / 3.6.2.1：
Distribution normalization / 3.6.2.2：
Analysis of Differentially-Expressed Genes / 3.7：
Basic Concepts in Statistics / 4.1：
Statistical Inference / 4.2.1：
Hypothesis Test / 4.2.2：
Fold Change Methods / 4.3：
k-fold Change / 4.3.1：
Unusual Ratios / 4.3.2：
Model-Based Methods / 4.3.3：
Parametric Tests / 4.4：
Paired t-Test / 4.4.1：
Unpaired t-Test / 4.4.2：
Variants of t-Test / 4.4.3：
Non-Parametric Tests / 4.5：
Classical Non-Parametric Statistics / 4.5.1：
Other Non-Parametric Statistics / 4.5.2：
Bootstrap Analysis / 4.5.3：
Multiple Testing / 4.6：
Family-Wise Error Rate / 4.6.1：
Sidak correction and Bonferroni correction / 4.6.1.1：
Holm's step-wise correction / 4.6.1.2：
False Discovery Rate / 4.6.2：
Permutation Correction / 4.6.3：
SAM: Significance Analysis of Microarrays / 4.6.4：
ANOVA: Analysis of Variance / 4.7：
One-Way ANOVA / 4.7.1：
Two-Way ANOVA / 4.7.2：
Gene-Based Analysis / 4.8：
Proximity Measurement for Gene Expression Data / 5.1：
Euclidean Distance / 5.2.1：
Correlation Coefficient / 5.2.2：
Pearson's correlation coefficient / 5.2.2.1：
Jackknife correlation / 5.2.2.2：
Spearman's rank-order correlation / 5.2.2.3：
Kullback-Leibler Divergence / 5.2.3：
Partition-Based Approaches / 5.3：
K-means and its Variations / 5.3.1：
SOM and its Extensions / 5.3.2：
Graph-Theoretical Approaches / 5.3.3：
HCS and CLICK / 5.3.3.1：
CAST: Cluster affinity search technique / 5.3.3.2：
Model-Based Clustering / 5.3.4：
Hierarchical Approaches / 5.4：
Agglomerative Algorithms / 5.4.1：
Divisive Algorithms / 5.4.2：
DAA: Deterministic annealing algorithm / 5.4.2.1：
SPC: Super-paramagnetic clustering / 5.4.2.2：
Density-Based Approaches / 5.5：
DBSCAN / 5.5.1：
OPTICS / 5.5.2：
DENCLUE / 5.5.3：
GPX: Gene Pattern eXplorer / 5.6：
The Attraction Tree / 5.6.1：
The distance measure / 5.6.1.1：
The density definition / 5.6.1.2：
The attraction tree / 5.6.1.3：
An example of attraction tree / 5.6.1.4：
Interactive Exploration of Coherent Patterns / 5.6.2：
Generating the index list / 5.6.2.1：
The coherent pattern index and its graph / 5.6.2.2：
Drilling down to subgroups / 5.6.2.3：
Experimental Results / 5.6.3：
Interactive exploration of Iyer's data and Spellman's data / 5.6.3.1：
Comparison with other algorithms / 5.6.3.2：
Efficiency and Scalability / 5.6.4：
Cluster Validation / 5.7：
Homogeneity and Separation / 5.7.1：
Agreement with Reference Partition / 5.7.2：
Reliability of Clusters / 5.7.3：
P-value of a cluster / 5.7.3.1：
Prediction strength / 5.7.3.2：
Sample-Based Analysis / 5.8：
Selection of Informative Genes / 6.1：
Supervised Approaches / 6.2.1：
Differentially expressed genes / 6.2.1.1：
Gene pairs / 6.2.1.2：
Virtual genes / 6.2.1.3：
Genetic algorithms / 6.2.1.4：
Unsupervised Approaches / 6.2.2：
PCA: Principal component analysis / 6.2.2.1：
Gene shaving / 6.2.2.2：
Class Prediction / 6.3：
Linear Discriminant Analysis / 6.3.1：
Instance-Based Classification / 6.3.2：
KNN: k-Nearest Neighbor / 6.3.2.1：
Weighted voting / 6.3.2.2：
Decision Trees / 6.3.3：
Support Vector Machines / 6.3.4：
Class Discovery / 6.4：
Problem statement / 6.4.1：
CLIFF: CLustering via Iterative Feature Filtering / 6.4.2：
The sample-partition process / 6.4.2.1：
The gene-filtering process / 6.4.2.2：
ESPD: Empirical Sample Pattern Detection / 6.4.3：
Measurements for phenotype structure detection / 6.4.3.1：
Algorithms / 6.4.3.2：
Experimental results / 6.4.3.3：
Classification Validation / 6.5：
Prediction Accuracy / 6.5.1：
Prediction Reliability / 6.5.2：
Pattern-Based Analysis / 6.6：
Mining Association Rules / 7.1：
Concepts of Association-Rule Mining / 7.2.1：
The Apriori Algorithm / 7.2.2：
The FP-Growth Algorithm / 7.2.3：
The CARPENTER Algorithm / 7.2.4：
Generating Association Rules in Microarray Data / 7.2.5：
Rule filtering / 7.2.5.1：
Rule grouping / 7.2.5.2：
Mining Pattern-Based Clusters in Microarray Data / 7.3：
Heuristic Approaches / 7.3.1：
Coupled two-way clustering (CTWC) / 7.3.1.1：
Plaid model / 7.3.1.2：
Biclustering and 5-Clusters / 7.3.1.3：
Deterministic Approaches / 7.3.2：
[delta]-pCluster / 7.3.2.1：
OP-Cluster / 7.3.2.2：
Mining Gene-Sample-Time Microarray Data / 7.4：
Three-dimensional Microarray Data / 7.4.1：
Coherent Gene Clusters / 7.4.2：
Problem description / 7.4.2.1：
Maximal coherent sample sets / 7.4.2.2：
The mining algorithms / 7.4.2.3：
Tri-Clusters / 7.4.2.4：
The tri-cluster model / 7.4.3.1：
Properties of tri-clusters / 7.4.3.2：
Mining tri-clusters / 7.4.3.3：
Visualization of Microarray Data / 7.5：
Single-Array Visualization / 8.1：
Box Plot / 8.2.1：
Histogram / 8.2.2：
Scatter Plot / 8.2.3：
Gene Pies / 8.2.4：
Multi-Array Visualization / 8.3：
Global Visualizations / 8.3.1：
Optimal Visualizations / 8.3.2：
Projection Visualization / 8.3.3：
VizStruct / 8.4：
Fourier Harmonic Projections / 8.4.1：
Discrete-time signal paradigm / 8.4.1.1：
The Fourier harmonic projection algorithm / 8.4.1.2：
Properties of FHPs / 8.4.2：
Basic properties / 8.4.2.1：
Advanced properties / 8.4.2.2：
Harmonic equivalency / 8.4.2.3：
Effects of harmonic twiddle power index / 8.4.2.4：
Enhancements of Fourier Harmonic Projections / 8.4.3：
Exploratory Visualization of Gene Profiling / 8.4.4：
Microarray data sets for visualization / 8.4.4.1：
Identification of informative genes / 8.4.4.2：
Classifier construction and evaluation / 8.4.4.3：
Dimension arrangement / 8.4.4.4：
Visualization of various data sets / 8.4.4.5：
Comparison of FFHP to Sammon's mapping / 8.4.4.6：
Confirmative Visualization of Gene Time-series / 8.4.5：
Data sets for visualization / 8.4.5.1：
The harmonic projection approach / 8.4.5.2：
Rat kidney data set / 8.4.5.3：
Yeast-A data set / 8.4.5.4：
Yeast-B data set / 8.4.5.5：
New Trends in Mining Gene Expression Microarray Data / 8.5：
Meta-Analysis of Microarray Data / 9.1：
Meta-Analysis of Differential Genes / 9.2.1：
Meta-Analysis of Co-Expressed Genes / 9.2.2：
Semi-Supervised Clustering / 9.3：
General Semi-Supervised Clustering Algorithms / 9.3.1：
A Seed-Generation Approach / 9.3.2：
Seed-generation methods / 9.3.2.1：
Pattern-selection rules / 9.3.2.2：
The framework for the seed-generation approach / 9.3.2.3：
Integration of Gene Expression Data with Other Data / 9.4：
A Probabilistic Model for Joint Mining / 9.4.1：
A Graph-Based Model for Joint Mining / 9.4.2：
Conclusion / 9.5：
Bibliography
Index
Preface
Introduction / 1：
The Microarray: Key to Functional Genomics and Systems Biology / 1.1：
5.

図書
図書
5. DNA microarrays and gene expression : from experiments to data analysis and modeling (:pb)
Pierre Baldi and G. Wesley Hatfield
出版情報: Cambridge : Cambridge University Press, 2011  xvi, 213 p. ; 24 cm
目次情報: 続きを見る
Preface
A brief history of genomics / 1：
DNA array formats / 2：
DNA array readout methods / 3：
Gene expression profiling experiments: problems, pitfalls and solutions / 4：
Statistical analysis of array data: inferring changes / 5：
Statistical analysis of array data: dimensionality reduction, clustering, and regulatory regions / 6：
Survey of current DNA array applications / 7：
Systems biology: overview of regulatory, metabolic and signaling networks / 8：
Preface
A brief history of genomics / 1：
DNA array formats / 2：
6.

図書
図書
6. Computational molecular biology : an algorithmic approach (: hc.)
Pavel A. Pevzner
出版情報: Cambridge, Mass. : MIT Press, c2000  xviii, 314 p. ; 24 cm
シリーズ名: Computational molecular biology
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