Contents

• Foreword
• Preface
• Acknowledgments
• 1. General Elements of Genomic Selection and Statistical Learning
  Published online: January 14, 2022.
  • 1.1. Data as a Powerful Weapon
  • 1.2. Genomic Selection
  • 1.3. Modeling Basics
  • 1.4. Matrix Algebra Review
  • 1.5. Statistical Data Types
  • 1.6. Types of Learning
  • References
• 2. Preprocessing Tools for Data Preparation
  Published online: January 14, 2022.
  • 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
• 3. Elements for Building Supervised Statistical Machine Learning Models
  Published online: January 14, 2022.
  • 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
  • 3.7. Logistic Regression
  • Appendix 1: R Code for Ridge Regression Used in Example 2
  • References
• 4. Overfitting, Model Tuning, and Evaluation of Prediction Performance
  Published online: January 14, 2022.
  • 4.1. The Problem of Overfitting and Underfitting
  • 4.2. The Trade-Off Between Prediction Accuracy and Model Interpretability
  • 4.3. Cross-validation
  • 4.4. Model Tuning
  • 4.5. Metrics for the Evaluation of Prediction Performance
  • References
• 5. Linear Mixed Models
  Published online: January 14, 2022.
  • 5.1. General of Linear Mixed Models
  • 5.2. Estimation of the Linear Mixed Model
  • 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
• 6. Bayesian Genomic Linear Regression
  Published online: January 14, 2022.
  • 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
  • 6.9. 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
  • References
• 7. Bayesian and Classical Prediction Models for Categorical and Count Data
  Published online: January 14, 2022.
  • 7.1. Introduction
  • 7.2. Bayesian Ordinal Regression Model
  • 7.3. Ordinal Logistic Regression
  • 7.4. Penalized Multinomial 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
• 8. Reproducing Kernel Hilbert Spaces Regression and Classification Methods
  Published online: January 14, 2022.
  • 8.1. The Reproducing Kernel Hilbert Spaces (RKHS)
  • 8.2. Generalized Kernel Model
  • 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
  • 8.9. Multi-trait Bayesian Kernel
  • 8.10. Kernel Compression Methods
  • 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
• 9. Support Vector Machines and Support Vector Regression
  Published online: January 14, 2022.
  • 9.1. Introduction to Support Vector Machine
  • 9.2. Hyperplane
  • 9.3. Maximum Margin Classifier
  • 9.4. Derivation of the Support Vector Classifier
  • 9.5. Support Vector Machine
  • 9.6. Support Vector Regression
  • Appendix 1
  • Appendix 2
  • Appendix 3
  • References
• 10. Fundamentals of Artificial Neural Networks and Deep Learning
  Published online: January 14, 2022.
  • 10.1. The Inspiration for the Neural Network Model
  • 10.2. The Building Blocks of Artificial Neural Networks
  • 10.3. Activation Functions
  • 10.4. The Universal Approximation Theorem
  • 10.5. Artificial Neural Network Topologies
  • 10.6. Successful Applications of ANN and DL
  • 10.7. Loss Functions
  • 10.8. The King Algorithm for Training Artificial Neural Networks: Backpropagation
  • References
• 11. Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes
  Published online: January 14, 2022.
  • 11.1. Hyperparameters to Be Tuned in ANN and DL
  • 11.2. Popular DL Frameworks
  • 11.3. Optimizers
  • 11.4. Illustrative Examples
  • Appendix 1
  • Appendix 2
  • Appendix 3
  • Appendix 4
  • Appendix 5
  • References
• 12. Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes
  Published online: January 14, 2022.
  • 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
  • Appendix 1
  • Appendix 2
  • Appendix 3
  • Appendix 4
  • Appendix 5
  • References
• 13. Convolutional Neural Networks
  Published online: January 14, 2022.
  • 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
• 14. Functional Regression
  Published online: January 14, 2022.
  • 14.1. Principles of Functional Linear Regression Analyses
  • 14.2. Basis Functions
  • 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
• 15. Random Forest for Genomic Prediction
  Published online: January 14, 2022.
  • 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
  • 15.5. 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

“This book is a prime example of CIMMYT’s commitment to scientific advancement, and comprehensive and inclusive knowledge sharing and dissemination. The book presents novel models and methods for genomic selection theory and aims to facilitate their adoption and use by publicly funded researchers and practitioners of National Agricultural Research Extension Systems (NARES) and universities across the Global South. The objectives of the authors is to offer an exhaustive overview of the current state of the art to give access to the different new models, methods, and techniques available to breeders who often struggle with limited resources and/or practical constraints when implementing genomics-assisted selection. This aspiration would not be possible without the continuous support of CIMMYT’s partners and donors who have funded non-profit frontier research for the benefit of millions of farmers and low-income communities worldwide. In this regard, it could not be more fitting for this book to be published as an open access resource for the international plant breeding community to benefit from. I trust that this publication will become a mandatory reference in the field of genomics-assisted breeding and that it will greatly contribute to accelerate the development and deployment of resource efficient and nutritious crops for a hotter and drier world.”

Bram Govaerts, Director General, a.i.

CIMMYT