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dc.contributor.authorMontesinos López, Osval Antonio
dc.contributor.authorMontesinos López, Abelardo
dc.contributor.authorCrossa, José
dc.date.accessioned2022-02-14T21:18:12Z
dc.date.available2022-02-14T21:18:12Z
dc.date.issued2022
dc.identifierONIX_20220214_9783030890100_13
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/52837
dc.description.abstractThis 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.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TV Agriculture & farming::TVB Agricultural science
dc.subject.classificationbic Book Industry Communication::P Mathematics & science::PS Biology, life sciences::PSA Life sciences: general issues
dc.subject.classificationbic Book Industry Communication::P Mathematics & science::PS Biology, life sciences::PST Botany & plant sciences
dc.subject.classificationbic Book Industry Communication::P Mathematics & science::PS Biology, life sciences::PSV Zoology & animal sciences::PSVH Animal reproduction
dc.subject.classificationbic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics
dc.subject.otheropen access
dc.subject.otherStatistical learning
dc.subject.otherBayesian regression
dc.subject.otherDeep learning
dc.subject.otherNon linear regression
dc.subject.otherPlant breeding
dc.subject.otherCrop management
dc.subject.othermulti-trait multi-environments models
dc.titleMultivariate Statistical Machine Learning Methods for Genomic Prediction
dc.typebook
oapen.identifier.doi10.1007/978-3-030-89010-0
oapen.relation.isPublishedBy6c6992af-b843-4f46-859c-f6e9998e40d5
oapen.relation.isFundedBy218ec580-e21b-49dd-92ef-e3cdeab38e7d
oapen.relation.isbn9783030890100
oapen.imprintSpringer International Publishing
oapen.pages691
oapen.place.publicationCham
oapen.grant.number[grantnumber unknown]


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