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020			_a9783030890100
024	7		_a10.1007/978-3-030-89010-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aS495 _b2022 EB
100			_aMontesinos López, Osval Antonio _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683299
245	0	0	_aMultivariate Statistical Machine Learning Methods for Genomic Prediction _cby Osval Antonio Montesinos López, Abelardo Montesinos López, José Crossa.
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXIV, 691 páginas) _b113 ilustraciones, 61 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
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.
988			_aSpringer_BiomedLife_2022
650		7	_9665994 _aAgricultura
700			_aMontesinos López, Abelardo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683300
700			_aCrossa, José _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683301
776	0	8	_iPrinted edition: _z9783030890094
776	0	8	_iPrinted edition: _z9783030890117
776	0	8	_iPrinted edition: _z9783030890124
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-89010-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _ep _zSI