دانلود کتاب Multivariate Statistical Machine Learning Methods for Genomic Prediction

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Foreword Preface Acknowledgments Contents Chapter 1: General Elements of Genomic Selection and Statistical Learning 1.1 Data as a Powerful Weapon 1.2 Genomic Selection 1.2.1 Concepts of Genomic Selection 1.2.2 Why Is Statistical Machine Learning a Key Element of Genomic Selection? 1.3 Modeling Basics 1.3.1 What Is a Statistical Machine Learning Model? 1.3.2 The Two Cultures of Model Building: Prediction Versus Inference 1.3.3 Types of Statistical Machine Learning Models and Model Effects 1.3.3.1 Types of Statistical Machine Learning Models 1.3.3.2 Model Effects 1.4 Matrix Algebra Review 1.5 Statistical Data Types 1.5.1 Data Types 1.5.2 Multivariate Data Types 1.6 Types of Learning 1.6.1 Definition and Examples of Supervised Learning 1.6.2 Definitions and Examples of Unsupervised Learning 1.6.3 Definition and Examples of Semi-Supervised Learning References Chapter 2: Preprocessing Tools for Data Preparation 2.1 Fixed or Random Effects 2.2 BLUEs and BLUPs 2.3 Marker Depuration 2.4 Methods to Compute the Genomic Relationship Matrix 2.5 Genomic Breeding Values and Their Estimation 2.6 Normalization Methods 2.7 General Suggestions for Removing or Adding Inputs 2.8 Principal Component Analysis as a Compression Method Appendix 1 Appendix 2 References Chapter 3: Elements for Building Supervised Statistical Machine Learning Models 3.1 Definition of a Linear Multiple Regression Model 3.2 Fitting a Linear Multiple Regression Model via the Ordinary Least Square (OLS) Method 3.3 Fitting the Linear Multiple Regression Model via the Maximum Likelihood (ML) Method 3.4 Fitting the Linear Multiple Regression Model via the Gradient Descent (GD) Method 3.5 Advantages and Disadvantages of Standard Linear Regression Models (OLS and MLR) 3.6 Regularized Linear Multiple Regression Model 3.6.1 Ridge Regression 3.6.2 Lasso Regression 3.7 Logistic Regression 3.7.1 Logistic Ridge Regression 3.7.2 Lasso Logistic Regression Appendix 1: R Code for Ridge Regression Used in Example 2 References Chapter 4: Overfitting, Model Tuning, and Evaluation of Prediction Performance 4.1 The Problem of Overfitting and Underfitting 4.2 The Trade-Off Between Prediction Accuracy and Model Interpretability 4.3 Cross-validation 4.3.1 The Single Hold-Out Set Approach 4.3.2 The k-Fold Cross-validation 4.3.3 The Leave-One-Out Cross-validation 4.3.4 The Leave-m-Out Cross-validation 4.3.5 Random Cross-validation 4.3.6 The Leave-One-Group-Out Cross-validation 4.3.7 Bootstrap Cross-validation 4.3.8 Incomplete Block Cross-validation 4.3.9 Random Cross-validation with Blocks 4.3.10 Other Options and General Comments on Cross-validation 4.4 Model Tuning 4.4.1 Why Is Model Tuning Important? 4.4.2 Methods for Hyperparameter Tuning (Grid Search, Random Search, etc.) 4.5 Metrics for the Evaluation of Prediction Performance 4.5.1 Quantitative Measures of Prediction Performance 4.5.2 Binary and Ordinal Measures of Prediction Performance 4.5.3 Count Measures of Prediction Performance References Chapter 5: Linear Mixed Models 5.1 General of Linear Mixed Models 5.2 Estimation of the Linear Mixed Model 5.2.1 Maximum Likelihood Estimation 5.2.1.1 EM Algorithm E Step M Step 5.2.1.2 REML 5.2.1.3 BLUPs 5.3 Linear Mixed Models in Genomic Prediction 5.4 Illustrative Examples of the Univariate LMM 5.5 Multi-trait Genomic Linear Mixed-Effects Models 5.6 Final Comments Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 Appendix 6 Appendix 7 References Chapter 6: Bayesian Genomic Linear Regression 6.1 Bayes Theorem and Bayesian Linear Regression 6.2 Bayesian Genome-Based Ridge Regression 6.3 Bayesian GBLUP Genomic Model 6.4 Genomic-Enabled Prediction BayesA Model 6.5 Genomic-Enabled Prediction BayesB and BayesC Models 6.6 Genomic-Enabled Prediction Bayesian Lasso Model 6.7 Extended Predictor in Bayesian Genomic Regression Models 6.8 Bayesian Genomic Multi-trait Linear Regression Model 6.8.1 Genomic Multi-trait Linear Model 6.9 Bayesian Genomic Multi-trait and Multi-environment Model (BMTME) Appendix 1 Appendix 2: Setting Hyperparameters for the Prior Distributions of the BRR Model Appendix 3: R Code Example 1 Appendix 4: R Code Example 2 Appendix 5 R Code Example 3 R Code for Example 4 References Chapter 7: Bayesian and Classical Prediction Models for Categorical and Count Data 7.1 Introduction 7.2 Bayesian Ordinal Regression Model 7.2.1 Illustrative Examples 7.3 Ordinal Logistic Regression 7.4 Penalized Multinomial Logistic Regression 7.4.1 Illustrative Examples for Multinomial Penalized Logistic Regression 7.5 Penalized Poisson Regression 7.6 Final Comments Appendix 1 Appendix 2 Appendix 3 Appendix 4 (Example 4) Appendix 5 Appendix 6 References Chapter 8: Reproducing Kernel Hilbert Spaces Regression and Classification Methods 8.1 The Reproducing Kernel Hilbert Spaces (RKHS) 8.2 Generalized Kernel Model 8.2.1 Parameter Estimation Under the Frequentist Paradigm 8.2.2 Kernels 8.2.3 Kernel Trick 8.2.4 Popular Kernel Functions 8.2.5 A Two Separate Step Process for Building Kernel Machines 8.3 Kernel Methods for Gaussian Response Variables 8.4 Kernel Methods for Binary Response Variables 8.5 Kernel Methods for Categorical Response Variables 8.6 The Linear Mixed Model with Kernels 8.7 Hyperparameter Tuning for Building the Kernels 8.8 Bayesian Kernel Methods 8.8.1 Extended Predictor Under the Bayesian Kernel BLUP 8.8.2 Extended Predictor Under the Bayesian Kernel BLUP with a Binary Response Variable 8.8.3 Extended Predictor Under the Bayesian Kernel BLUP with a Categorical Response Variable 8.9 Multi-trait Bayesian Kernel 8.10 Kernel Compression Methods 8.10.1 Extended Predictor Under the Approximate Kernel Method 8.11 Final Comments Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 Appendix 6 Appendix 7 Appendix 8 Appendix 9 Appendix 10 Appendix 11 References Chapter 9: Support Vector Machines and Support Vector Regression 9.1 Introduction to Support Vector Machine 9.2 Hyperplane 9.3 Maximum Margin Classifier 9.3.1 Derivation of the Maximum Margin Classifier 9.3.2 Wolfe Dual 9.4 Derivation of the Support Vector Classifier 9.5 Support Vector Machine 9.5.1 One-Versus-One Classification 9.5.2 One-Versus-All Classification 9.6 Support Vector Regression Appendix 1 Appendix 2 Appendix 3 References Chapter 10: Fundamentals of Artificial Neural Networks and Deep Learning 10.1 The Inspiration for the Neural Network Model 10.2 The Building Blocks of Artificial Neural Networks 10.3 Activation Functions 10.3.1 Linear 10.3.2 Rectifier Linear Unit (ReLU) 10.3.3 Leaky ReLU 10.3.4 Sigmoid 10.3.5 Softmax 10.3.6 Tanh 10.4 The Universal Approximation Theorem 10.5 Artificial Neural Network Topologies 10.6 Successful Applications of ANN and DL 10.7 Loss Functions 10.7.1 Loss Functions for Continuous Outcomes 10.7.2 Loss Functions for Binary and Ordinal Outcomes 10.7.3 Regularized Loss Functions 10.7.4 Early Stopping Method of Training 10.8 The King Algorithm for Training Artificial Neural Networks: Backpropagation 10.8.1 Backpropagation Algorithm: Online Version 10.8.1.1 Feedforward Part 10.8.1.2 Backpropagation Part 10.8.2 Illustrative Example 10.1: A Hand Computation 10.8.3 Illustrative Example 10.2-By Hand Computation References Chapter 11: Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes 11.1 Hyperparameters to Be Tuned in ANN and DL 11.1.1 Network Topology 11.1.2 Activation Functions 11.1.3 Loss Function 11.1.4 Number of Hidden Layers 11.1.5 Number of Neurons in Each Layer 11.1.6 Regularization Type 11.1.7 Learning Rate 11.1.8 Number of Epochs and Number of Batches 11.1.9 Normalization Scheme for Input Data 11.2 Popular DL Frameworks 11.3 Optimizers 11.4 Illustrative Examples Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 References Chapter 12: Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes 12.1 Training DNN with Binary Outcomes 12.2 Training DNN with Categorical (Ordinal) Outcomes 12.3 Training DNN with Count Outcomes 12.4 Training DNN with Multivariate Outcomes 12.4.1 DNN with Multivariate Continuous Outcomes 12.4.2 DNN with Multivariate Binary Outcomes 12.4.3 DNN with Multivariate Ordinal Outcomes 12.4.4 DNN with Multivariate Count Outcomes 12.4.5 DNN with Multivariate Mixed Outcomes Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 References Chapter 13: Convolutional Neural Networks 13.1 The Importance of Convolutional Neural Networks 13.2 Tensors 13.3 Convolution 13.4 Pooling 13.5 Convolutional Operation for 1D Tensor for Sequence Data 13.6 Motivation of CNN 13.7 Why Are CNNs Preferred over Feedforward Deep Neural Networks for Processing Images? 13.8 Illustrative Examples 13.9 2D Convolution Example 13.10 Critics of Deep Learning Appendix 1 Appendix 2 References Chapter 14: Functional Regression 14.1 Principles of Functional Linear Regression Analyses 14.2 Basis Functions 14.2.1 Fourier Basis 14.2.2 B-Spline Basis 14.3 Illustrative Examples 14.4 Functional Regression with a Smoothed Coefficient Function 14.5 Bayesian Estimation of the Functional Regression Appendix 1 Appendix 2 (Example 14.4) Appendix 3 (Example 14.5) Appendix 4 (Example 14.6) References Chapter 15: Random Forest for Genomic Prediction 15.1 Motivation of Random Forest 15.2 Decision Trees 15.3 Random Forest 15.4 RF Algorithm for Continuous, Binary, and Categorical Response Variables 15.4.1 Splitting Rules 15.5 RF Algorithm for Count Response Variables 15.6 RF Algorithm for Multivariate Response Variables 15.7 Final Comments Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 Appendix 6 References Index