Multivariate statistical machine learning methods for genomic prediction / Osval Antonio Montesinos López, Abelardo Montesinos López, Jose Crossa.
Montesinos López, Osval Antonio, author.; Montesinos López, Abelardo, author.; Crossa, José, author.
2022
QK981.5
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Title Multivariate statistical machine learning methods for genomic prediction / Osval Antonio Montesinos López, Abelardo Montesinos López, Jose Crossa.
Author Montesinos López, Osval Antonio, author.
ISBN 9783030890100 (ebook)
3030890104
9783030890094
3030890090
DOI https://doi.org/10.1007/978-3-030-89010-0
Published Cham, Switzerland : Springer, [2022]
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-030-89010-0 doi
Call Number QK981.5
Dewey Decimal Classification 581.3/50727
Summary 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Open access.
Digital File Characteristics text file PDF
Source of Description Description based on online resource; title from PDF title page (viewed December 15, 2022).
Added Author Montesinos López, Abelardo, author.
Crossa, José, author.
In Springer Nature eBook
Available in Other Form Print version: 9783030890094
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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.

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