Data mining for genomics and proteomics : analysis of gene and protein expression data, Darius M. Dzuida

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The instance Data mining for genomics and proteomics : analysis of gene and protein expression data, Darius M. Dzuida represents a material embodiment of a distinct intellectual or artistic creation found in Massey University Library, University of New Zealand.

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Data mining for genomics and proteomics : analysis of gene and protein expression data, Darius M. Dzuida

Title remainder

analysis of gene and protein expression data

Statement of responsibility

Darius M. Dzuida

Instantiates

• Data mining for genomics and proteomics : analysis of gene and protein expression data

Publication

• Hoboken, N.J., Wiley, c2010

Bibliography note

Includes bibliographical references (p. 289-306) and index

Contents

• 1. Introduction -- 1.1. Basic Terminology -- 1.1.1. The Central Dogma of Molecular Biology -- 1.1.2. Genome -- 1.1.3. Proteome -- 1.1.4. DNA (Deoxyribonucleic Acid) -- 1.1.5. RNA (Ribonucleic Acid) -- 1.1.6. mRNA (messenger RNA) -- 1.1.7. Genetic Code -- 1.1.8. Gene -- 1.1.9. Gene Expression and the Gene Expression Level -- 1.1.10. Protein -- 1.2. Overlapping Areas of Research -- 1.2.1. Genomics -- 1.2.2. Proteomics -- 1.2.3. Bioinformatics -- 1.2.4. Transcriptomics and Other-omics -- 1.2.5. Data Mining -- 2. Basic Analysis Of Gene Expression Microarray Data -- 2.1. Introduction -- 2.2. Microarray Technology -- 2.2.1. Spotted Microarrays -- 2.2.2. Affymetrix GeneChip ® Microarrays -- 2.2.3. Bead-Based Microarrays -- 2.3. Low-Level Preprocessing of Affymetrix Microarrays -- 2.3.1. MASS -- 2.3.2. RMA -- 2.3.3. GCRMA -- 2.3.4. PLIER -- 2.4. Public Repositories of Microarray Data -- 2.4.1. Microarray Gene Expression Data Society (MGED) Standards -- 2.4.2. Public Databases -- 2.4.2.1. Gene Expression Omnibus (GEO) -- 2.4.2.2. ArrayExpress -- 2.5. Gene Expression Matrix -- 2.5.1. Elements of Gene Expression Microarray Data Analysis -- 2.6. Additional Preprocessing, Quality Assessment, and Filtering -- 2.6.1. Quality Assessment -- 2.6.2. Filtering -- 2.7. Basic Exploratory Data Analysis -- 2.7.1. t Test -- 2.7.1.1. t Test for Equal Variances -- 2.7.1.2. t Test for Unequal Variances -- 2.7.2. ANOVA F Test -- 2.7.3. SAM t Statistic -- 2.7.4. Limma -- 2.7.5. Adjustment for Multiple Comparisons -- 2.7.5.1. Single-Step Bonferroni Procedure -- 2.7.5.2. Single-Step Sidak Procedure -- 2.7.5.3. Step-Down Holm Procedure -- 2.7.5.4. Step-Up Benjamini and Hochberg Procedure -- 2.7.5.5. Permutation Based Multiplicity Adjustment -- 2.8. Unsupervised Learning (Taxonomy-Related Analysis) -- 2.8.1. Cluster Analysis -- 2.8.1.1. Measures of Similarity or Distance -- 2.8.1.2. k-Means Clustering -- 2.8.1.3. Hierarchical Clustering -- 2.8.1.4. Two-Way Clustering and Related Methods -- 2.8.2. Principal Component Analysis -- 2.8.3. Self-Organizing Maps -- Exercises -- 3. Biomarker Discovery and Classification -- 3.1. Overview -- 3.1.1. Gene Expression Matrix...Again -- 3.1.2. Biomarker Discovery -- 3.1.3. Classification Systems -- 3.1.3.1. Parametric and Nonparametric Learning Algorithms -- 3.1.3.2. Terms Associated with Common Assumptions Underlying Parametric Learning Algorithms -- 3.1.3.3. Visualization of Classification Results -- 3.1.4. Validation of the Classification Model -- 3.1.4.1. Reclassification -- 3.1.4.2. Leave-One-Out and K-Fold Cross-Validation -- 3.1.4.3. External and Internal Cross-Validation -- 3.1.4.4. Holdout Method of Validation -- 3.1.4.5. Ensemble-Based Validation (Using Out-of-Bag Samples) -- 3.1.4.6. Validation on an Independent Data Set -- 3.1.5. Reporting Validation Results -- 3.1.5.1. Binary Classifiers -- 3.1.5.2. Multiclass Classifiers -- 3.1.6. Identifying Biological Processes Underlying the Class Differentiation -- 3.2. Feature Selection -- 3.2.1. Introduction -- 3.2.2. Univariate Versus Multivariate Approaches -- 3.2.3. Supervised Versus Unsupervised Methods -- 3.2.4. Taxonomy of Feature Selection Methods -- 3.2.4.1. Filters, Wrappers, Hybrid, and Embedded Models -- 3.2.4.2. Strategy: Exhaustive, Complete, Sequential, Random, and Hybrid Searches -- 3.2.4.3. Subset Evaluation Criteria -- 3.2.4.4. Search-Stopping Criteria -- 3.2.5. Feature Selection for Multiclass Discrimination -- 3.2.6. Regularization and Feature Selection -- 3.2.7. Stability of Biomarkers -- 3.3. Discriminant Analysis -- 3.3.1. Introduction -- 3.3.2. Learning Algorithm -- 3.3.3. A Stepwise Hybrid Feature Selection with T2 -- 3.4. Support Vector Machines -- 3.4.1. Hard-Margin Support Vector Machines -- 3.4.2. Soft-Margin Support Vector Machines -- 3.4.3. Kernels -- 3.4.4. SVMs and Multiclass Discrimination -- 3.4.4.1. One-Versus-the-Rest Approach -- 3.4.4.2. Pairwise Approach -- 3.4.4.3. All-Classes-Simultaneously Approach -- 3.4.5. SVMs and Feature Selection: Recursive Feature Elimination -- 3.4.6. Summary -- 3.5. Random Forests -- 3.5.1. Introduction -- 3.5.2. Random Forests Learning Algorithm -- 3.5.3. Random Forests and Feature Selection -- 3.5.4. Summary -- 3.6. Ensemble Classifiers, Bootstrap Methods, and The Modified Bagging Schema -- 3.6.1. Ensemble Classifiers -- 3.6.1.1. Parallel Approach -- 3.6.1.2. Serial Approach -- 3.6.1.3. Ensemble Classifiers and Biomarker Discovery -- 3.6.2. Bootstrap Methods -- 3.6.3. Bootstrap and Linear Discriminant Analysis -- 3.6.4. The Modified Bagging Schema -- 3.7. Other Learning Algorithms -- 3.7.1. k-Nearest Neighbor Classifiers -- 3.7.2. Artificial Neural Networks -- 3.7.2.1. Perceptron -- 3.7.2.2. Multilayer Feedforward Neural Networks -- 3.7.2.3. Training the Network (Supervised Learning) -- 3.8. Eight Commandments of Gene Expression Analysis (for Biomarker Discovery) -- Exercises -- 4. The Informative Set of Genes -- 4.1. Introduction -- 4.2. Definitions -- 4.3. The Method -- 4.3.1. Identification of the Informative Set of Genes -- 4.3.2. Primary Expression Patterns of the informative Set of Genes -- 4.3.3. The Most Frequently Used Genes of the Primary Expression Patterns -- 4.4. Using the Informative Set of Genes to Identify Robust Multivariate Biomarkers -- 4.5. Summary -- Exercises -- 5. Analysis of Protein Expression Data -- 5.1. Introduction -- 5.2. Protein Chip Technology -- 5.2.1. Antibody Microarrays -- 5.2.2. Peptide Microarrays -- 5.2.3. Protein Microarrays -- 5.2.4. Reverse Phase Microarrays -- 5.3. Two-Dimensional Gel Electrophoresis -- 5.4. MALDI-TOF and SELDI-TOF Mass Spectrometry -- 5.4.1. MALDI-TOF Mass Spectrometry -- 5.4.2. SELDI-TOF Mass Spectrometry -- 5.5. Preprocessing of Mass Spectrometry Data -- 5.5.1. Introduction -- 5.5.2. Elements of Preprocessing of SELDI-TOF Mass Spectrometry Data -- 5.5.2.1. Quality Assessment -- 5.5.2.2. Calibration -- 5.5.2.3. Baseline Correction -- 5.5.2.4. Noise Reduction and Smoothing -- 5.5.2.5. Peak Detection -- 5.5.2.6. Intensity Normalization -- 5.5.2.7. Peak Alignment Across Spectra -- 5.6. Analysis of Protein Expression Data -- 5.6.1. Additional Preprocessing -- 5.6.2. Basic Exploratory Data Analysis -- 5.6.3. Unsupervised Learning -- 5.6.4. Supervised Learning---Feature Selection and Biomarker Discovery -- 5.6.5. Supervised Learning---Classification Systems -- 5.7. Associating Biomarker Peaks with Proteins -- 5.7.1. Introduction -- 5.7.2. The Universal Protein Resource (UniProt) -- 5.7.3. Search Programs -- 5.7.4. Tandem Mass Spectrometry -- 5.8. Summary -- 6. Sketches for Selected Exercises -- 6.1. Introduction -- 6.2. Multiclass Discrimination (Exercise 3.2) -- 6.2.1. Data Set Selection, Downloading, and Consolidation -- 6.2.2. Filtering Probe Sets -- 6.2.3. Designing a Multistage Classification Schema -- 6.3. Identifying the Informative Set of Genes (Exercises 4.2-4.6) -- 6.3.1. The Informative Set of Genes -- 6.3.2. Primary Expression Patterns of the Informative Set -- 6.3.3. The Most Frequently Used Genes of the Primary Expression Patterns -- 6.4. Using the Informative Set of Genes to Identify Robust Multivariate Markers (Exercise 4.8) -- 6.5. Validating Biomarkers on an Independent Test Data Set (Exercise 4.8) -- 6.6. Using a Training Set that Combines More than One Data Set (Exercises 3.5 and 4.1-4.8) -- 6.6.1. Combining the Two Data Sets into a Single Training Set -- 6.6.2. Filtering Probe Sets of the Combined Data -- 6.6.3. Assessing the Discriminatory Power of the Biomarkers and Their Generalization -- 6.6.4. Identifying the Informative Set of Genes --
• 6.6.5. Primary Expression Patterns of the Informative Set of Genes -- 6.6.6. The Most Frequently Used Genes of the Primary Expression Patterns -- 6.6.7. Using the Informative Set of Genes to Identify Robust Multivariate Markers -- 6.6.8. Validating Biomarkers on an Independent Test Data Set

Control code

ocn460048996

Dimensions

25 cm

Extent

xvii, 319 p., [8] p. of plates

Isbn

9780470163733

Isbn Type

(cloth)

Lccn

2009052129

Other physical details

ill. (some col.)

Record ID

.b23451658

System control number

(OCoLC)460048996