Multivariate Statistical Machine Learning Methods for Genomic Prediction
Publication year: 2022
ISBN: 978-3-030-89010-0
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This 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.
Subject: Biomedical and Life Sciences, Agriculture, Biostatistics, Plant Genetics and Genomics, Animal Genetics and Genomics, Statistics for Life Sciences, Medicine, Health Sciences, Statistical learning, Bayesian regression, Deep learning, Non linear regression, Plant breeding, Crop management
