Leif E. Peterson

Analysis of DNA Microarrays

Herausgeber: Pan, Yi; Zomaya, Albert Y

• Gebundenes Buch

Produktbeschreibung

The rapid uncontrolled growth of classification methods in DNA microarray studies has resulted in a body of information scattered throughout literature, numerous conference proceedings, and others. This book brings together many of the unsupervised and supervised classification methods now dispersed in the literature. It breaks away from traditional statistical methods by providing chapters on newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, and swarm intelligence involving particle swarm optimization and ant colony optimization.

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Wide coverage of traditional unsupervised and supervised methods and newer contemporary approaches that help researchers handle the rapid growth of classification methods in DNA microarray studies

Proliferating classification methods in DNA microarray studies have resulted in a body of information scattered throughout literature, conference proceedings, and elsewhere. This book unites many of these classification methods in a single volume. In addition to traditional statistical methods, it covers newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, swarm intelligence involving particle swarm optimization, and more.

Classification Analysis of DNA Microarrays provides highly detailed pseudo-code and rich, graphical programming features, plus ready-to-run source code. Along with primary methods that include traditional and contemporary classification, it offers supplementary tools and data preparation routines for standardization and fuzzification; dimensional reduction via crisp and fuzzy c-means, PCA, and non-linear manifold learning; and computational linguistics via text analytics and n-gram analysis, recursive feature extraction during ANN, kernel-based methods, ensemble classifier fusion.

This powerful new resource:
Provides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studies
Serves as a historical repository of general use supervised classification methods as well as newer contemporary methods
Brings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the book
Describes implementation methods that help shorten discovery times

Classification Analysis of DNA Microarrays is useful for professionals and graduate students in computer science, bioinformatics, biostatistics, systems biology, and many related fields.

Produktdetails

• Wiley Series in Bioinformatics
• Verlag: IEEE Press / Wiley & Sons
• 1. Auflage
• Seitenzahl: 736
• Erscheinungstermin: Mai 2013
• Englisch
• Abmessung: 240mm x 161mm x 45mm
• Gewicht: 1285g
• ISBN-13: 9780470170816
• ISBN-10: 0470170816
• Artikelnr.: 26172588

Autorenporträt

LEIF E. PETERSON, PHD, is Associate Professor of Public Health, Weill Cornell Medical College, Cornell University, and is with the Center for Biostatistics, The Methodist Hospital Research Institute (Houston). He is a member of the IEEE Computational Intelligence Society, and Editor-in-Chief of the BioMed Central Source Code for Biology and Medicine.

Inhaltsangabe

Preface xix Abbreviations xxiii 1 Introduction 1 1.1 Class Discovery 2 1.2
Dimensional Reduction 4 1.3 Class Prediction 4 1.4 Classification Rules of
Thumb 5 1.5 DNA Microarray Datasets Used 9 References 11 PART I CLASS
DISCOVERY 13 2 Crisp K-Means Cluster Analysis 15 2.1 Introduction 15 2.2
Algorithm 16 2.3 Implementation 18 2.4 Distance Metrics 20 2.5 Cluster
Validity 24 2.6 V-Fold Cross-Validation 35 2.7 Cluster Initialization 37
2.8 Cluster Outliers 44 2.9 Summary 44 References 45 3 Fuzzy K-Means
Cluster Analysis 47 3.1 Introduction 47 3.2 Fuzzy K-Means Algorithm 47 3.3
Implementation 49 3.4 Summary 54 References 54 4 Self-Organizing Maps 57
4.1 Introduction 57 4.2 Algorithm 57 4.3 Implementation 63 4.4 Cluster
Visualization 67 4.5 Unified Distance Matrix (U Matrix) 71 4.6 Component
Map 71 4.7 Map Quality 73 4.8 Nonlinear Dimension Reduction 75 References
79 5 Unsupervised Neural Gas 81 5.1 Introduction 81 5.2 Algorithm 82 5.3
Implementation 82 5.4 Nonlinear Dimension Reduction 85 5.5 Summary 87
References 88 6 Hierarchical Cluster Analysis 91 6.1 Introduction 91 6.2
Methods 91 6.3 Algorithm 96 6.4 Implementation 96 References 105 7
Model-Based Clustering 107 7.1 Introduction 107 7.2 Algorithm 110 7.3
Implementation 111 7.4 Summary 116 References 117 8 Text Mining: Document
Clustering 119 8.1 Introduction 119 8.2 Duo-Mining 119 8.3 Streams and
Documents 120 8.4 Lexical Analysis 120 8.5 Stemming 121 8.6 Term Weighting
121 8.7 Concept Vectors 124 8.8 Main Terms Representing Concept Vectors 124
8.9 Algorithm 125 8.10 Preprocessing 127 8.11 Summary 137 References 137 9
Text Mining: N-Gram Analysis 139 9.1 Introduction 139 9.2 Algorithm 140 9.3
Implementation 141 9.4 Summary 154 References 156 PART II DIMENSION
REDUCTION 159 10 Principal Components Analysis 161 10.1 Introduction 161
10.2 Multivariate Statistical Theory 161 10.3 Algorithm 170 10.4 When to
Use Loadings and PC Scores 170 10.5 Implementation 171 10.6 Rules of Thumb
For PCA 182 10.7 Summary 186 References 187 11 Nonlinear Manifold Learning
189 11.1 Introduction 189 11.2 Correlation-Based PCA 190 11.3 Kernel PCA
191 11.4 Diffusion Maps 192 11.5 Laplacian Eigenmaps 192 11.6 Local Linear
Embedding 193 11.7 Locality Preserving Projections 194 11.8 Sammon Mapping
195 11.9 NLML Prior to Classification Analysis 195 11.10 Classification
Results 197 11.11 Summary 200 References 203 PART III CLASS PREDICTION 205
12 Feature Selection 207 12.1 Introduction 207 12.2 Filtering versus
Wrapping 208 12.3 Data 209 12.4 Data Arrangement 211 12.5 Filtering 213
12.6 Selection Methods 254 12.7 Multicollinearity 259 12.8 Summary 270
References 270 13 Classifier Performance 273 13.1 Introduction 273 13.2
Input-Output, Speed, and Efficiency 273 13.3 Training, Testing, and
Validation 277 13.4 Ensemble Classifier Fusion 280 13.5 Sensitivity and
Specificity 283 13.6 Bias 284 13.7 Variance 285 13.8 Receiver-Operator
Characteristic (ROC) Curves 286 References 295 14 Linear Regression 297
14.1 Introduction 297 14.2 Algorithm 299 14.3 Implementation 299 14.4
Cross-Validation Results 300 14.5 Bootstrap Bias 303 14.6 Multiclass ROC
Curves 306 14.7 Decision Boundaries 308 14.8 Summary 310 References 310 15
Decision Tree Classification 311 15.1 Introduction 311 15.2 Features Used
314 15.3 Terminal Nodes and Stopping Criteria 315 15.4 Algorithm 315 15.5
Implementation 315 15.6 Cross-Validation Results 318 15.7 Decision
Boundaries 326 15.8 Summary 327 References 329 16 Random Forests 331 16.1
Introduction 331 16.2 Algorithm 333 16.3 Importance Scores 334 16.4
Strength and Correlation 338 16.5 Proximity and Supervised Clustering 342
16.6 Unsupervised Clustering 345 16.7 Class Outlier Detection 348 16.8
Implementation 350 16.9 Parameter Effects 350 16.10 Summary 357 References
358 17 K Nearest Neighbor 361 17.1 Introduction 361 17.2 Algorithm 362 17.3
Implementation 363 17.4 Cross-Validation Results 364 17.5 Bootstrap Bias
369 17.6 Multiclass ROC Curves 373 17.7 Decision Boundaries 374 17.8
Summary 377 References 378 18 Nä?ve Bayes Classifier 379 18.1 Introduction
379 18.2 Algorithm 380 18.3 Cross-Validation Results 380 18.4 Bootstrap
Bias 384 18.5 Multiclass ROC Curves 386 18.6 Decision Boundaries 386 18.7
Summary 389 References 391 19 Linear Discriminant Analysis 393 19.1
Introduction 393 19.2 Multivariate Matrix Definitions 394 19.3 Linear
Discriminant Analysis 396 19.4 Quadratic Discriminant Analysis 403 19.5
Fisher's Discriminant Analysis 406 19.6 Summary 411 References 412 20
Learning Vector Quantization 415 20.1 Introduction 415 20.2
Cross-Validation Results 417 20.3 Bootstrap Bias 417 20.4 Multiclass ROC
Curves 426 20.5 Decision Boundaries 428 20.6 Summary 428 References 430 21
Logistic Regression 433 21.1 Introduction 433 21.2 Binary Logistic
Regression 434 21.3 Polytomous Logistic Regression 439 21.4
Cross-Validation Results 443 21.5 Decision Boundaries 444 21.6 Summary 444
References 447 22 Support Vector Machines 449 22.1 Introduction 449 22.2
Hard-Margin SVM for Linearly Separable Classes 449 22.3 Kernel Mapping into
Nonlinear Feature Space 452 22.4 Soft-Margin SVM for Nonlinearly Separable
Classes 452 22.5 Gradient Ascent Soft-Margin SVM 454 22.6 Least-Squares
Soft-Margin SVM 465 22.7 Summary 481 References 483 23 Artificial Neural
Networks 487 23.1 Introduction 487 23.2 ANN Architecture 488 23.3 Basics of
ANN Training 488 23.4 ANN Training Methods 497 23.5 Algorithm 502 23.6
Batch versus Online Training 504 23.7 ANN Testing 504 23.8 Cross-Validation
Results 504 23.9 Bootstrap Bias 506 23.10 Multiclass ROC Curves 506 23.11
Decision Boundaries 513 23.12 RPROP versus Backpropagation 513 23.13
Summary 522 References 522 24 Kernel Regression 525 24.1 Introduction 525
24.2 Algorithm 527 24.3 Cross-Validation Results 527 24.4 Bootstrap Bias
528 24.5 Multiclass ROC Curves 536 24.6 Decision Boundaries 537 24.7
Summary 540 References 542 25 Neural Adaptive Learning with Metaheuristics
543 25.1 Multilayer Perceptrons 544 25.2 Genetic Algorithms 544 25.3
Covariance Matrix Self-Adaptation-Evolution Strategies 549 25.4 Particle
Swarm Optimization 556 25.5 ANT Colony Optimization 560 25.6 Summary 567
References 567 26 Supervised Neural Gas 573 26.1 Introduction 573 26.2
Algorithm 574 26.3 Cross-Validation Results 574 26.4 Bootstrap Bias 582
26.5 Multiclass ROC Curves 582 26.6 Class Decision Boundaries 584 26.7
Summary 586 References 588 27 Mixture of Experts 591 27.1 Introduction 591
27.2 Algorithm 595 27.3 Cross-Validation Results 596 27.4 Decision
Boundaries 597 27.5 Summary 597 References 599 28 Covariance Matrix
Filtering 601 28.1 Introduction 601 28.2 Covariance and Correlation
Matrices 601 28.3 Random Matrices 602 28.4 Component Subtraction 608 28.5
Covariance Matrix Shrinkage 610 28.6 Covariance Matrix Filtering 613 28.7
Summary 621 References 622 APPENDIXES 625 A Probability Primer 627 A.1
Choices 627 A.2 Permutations 628 A.3 Combinations 630 A.4 Probability 632 B
Matrix Algebra 639 B.1 Vectors 639 B.2 Matrices 642 B.3 Sample Mean,
Covariance, and Correlation 647 B.4 Diagonal Matrices 648 B.5 Identity
Matrices 649 B.6 Trace of a Matrix 650 B.7 Eigenanalysis 650 B.8 Symmetric
Eigenvalue Problem 650 B.9 Generalized Eigenvalue Problem 651 B.10 Matrix
Properties 652 C Mathematical Functions 655 C.1 Inequalities 655 C.2 Laws
of Exponents 655 C.3 Laws of Radicals 656 C.4 Absolute Value 656 C.5
Logarithms 656 C.6 Product and Summation Operators 657 C.7 Partial
Derivatives 657 C.8 Likelihood Functions 658 D Statistical Primitives 665
D.1 Rules of Thumb 665 D.2 Primitives 668 References 678 E Probability
Distributions 679 E.1 Basics of Hypothesis Testing 679 E.2 Probability
Functions: Source of p Values 682 E.3 Normal Distribution 682 E.4 Gamma
Function 686 E.5 Beta Function 689 E.6 Pseudo-Random-Number Generation 692
References 698 F Symbols And Notation 699 Index 703