Multivariate Statistical Machine Learning Methods for Genomic Prediction / by Osval Antonio Montesinos López, Abelardo Montesinos López, José Crossa.

Format:

Book

Author/Creator:

Montesinos López, Osval Antonio.

Contributor:

Montesinos López, Abelardo.

Crossa, José.

Language:

English

Subjects (All):

Agriculture.

Bioinformatics.

Plant genetics.

Agricultural genome mapping.

Biometry.

Plant Genetics.

Agricultural Genetics.

Biostatistics.

Local Subjects:

Agriculture.

Bioinformatics.

Plant Genetics.

Agricultural Genetics.

Biostatistics.

Physical Description:

1 online resource (707 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

Language Note:

English

Summary:

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Contents:

Preface

Chapter 1

General elements of genomic selection and statistical learning

Chapter. 2

Preprocessing tools for data preparation

Chapter. 3

Elements for building supervised statistical machine learning models

Chapter. 4

Overfitting, model tuning and evaluation of prediction performance

Chapter. 5

Linear Mixed Models

Chapter. 6

Bayesian Genomic Linear Regression

Chapter. 7

Bayesian and classical prediction models for categorical and count data

Chapter. 8

Reproducing Kernel Hilbert Spaces Regression and Classification Methods

Chapter. 9

Support vector machines and support vector regression

Chapter. 10

Fundamentals of artificial neural networks and deep learning

Chapter. 11

Artificial neural networks and deep learning for genomic prediction of continuous outcomes

Chapter. 12

Artificial neural networks and deep learning for genomic prediction of binary, ordinal and mixed outcomes

Chapter. 13

Convolutional neural networks

Chapter. 14

Functional regression

Chapter. 15

Random forest for genomic prediction.

ISBN:

3-030-89010-4

OCLC:

1294143848