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020 _a9783030890100
_9978-3-030-89010-0
024 7 _a10.1007/978-3-030-89010-0
_2doi
050 4 _aS1-972
072 7 _aTVB
_2bicssc
072 7 _aTEC003000
_2bisacsh
072 7 _aTVB
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082 0 4 _a630
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100 1 _aMontesinos López, Osval Antonio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMultivariate Statistical Machine Learning Methods for Genomic Prediction
_h[electronic resource] /
_cby Osval Antonio Montesinos López, Abelardo Montesinos López, José Crossa.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXXIV, 691 p. 113 illus., 61 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPreface -- 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.
506 0 _aOpen Access
520 _aThis 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.
650 0 _aAgriculture.
650 0 _aBioinformatics.
650 0 _aPlant genetics.
650 0 _aAgricultural genome mapping.
650 0 _aBiometry.
650 1 4 _aAgriculture.
650 2 4 _aBioinformatics.
650 2 4 _aPlant Genetics.
650 2 4 _aAgricultural Genetics.
650 2 4 _aBiostatistics.
700 1 _aMontesinos López, Abelardo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aCrossa, José.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030890094
776 0 8 _iPrinted edition:
_z9783030890117
776 0 8 _iPrinted edition:
_z9783030890124
856 4 0 _uhttps://doi.org/10.1007/978-3-030-89010-0
912 _aZDB-2-SBL
912 _aZDB-2-SXB
912 _aZDB-2-SOB
999 _c72
_d72