LEADER 04592nam 22007455 450 001 9910522999103321 005 20251126130405.0 010 $a3-030-89010-4 024 7 $a10.1007/978-3-030-89010-0 035 $a(CKB)5100000000193939 035 $a(MiAaPQ)EBC6855260 035 $a(Au-PeEL)EBL6855260 035 $a(OCoLC)1294143848 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/78249 035 $a(PPN)260834084 035 $a(ODN)ODN0010072106 035 $a(oapen)doab78249 035 $a(DNLM)9918470283606676 035 $a(DE-He213)978-3-030-89010-0 035 $a(EXLCZ)995100000000193939 100 $a20220113d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultivariate Statistical Machine Learning Methods for Genomic Prediction /$fby Osval Antonio Montesinos López, Abelardo Montesinos López, José Crossa 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (707 pages) 311 08$a3-030-89009-0 327 $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. 330 $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. 606 $aAgriculture 606 $aBioinformatics 606 $aPlant genetics 606 $aAgricultural genome mapping 606 $aBiometry 606 $aAgriculture 606 $aBioinformatics 606 $aPlant Genetics 606 $aAgricultural Genetics 606 $aBiostatistics 615 0$aAgriculture. 615 0$aBioinformatics. 615 0$aPlant genetics. 615 0$aAgricultural genome mapping. 615 0$aBiometry. 615 14$aAgriculture. 615 24$aBioinformatics. 615 24$aPlant Genetics. 615 24$aAgricultural Genetics. 615 24$aBiostatistics. 676 $a630 686 $aMED090000$aSCI011000$aSCI070000$aSCI086000$aTEC003000$2bisacsh 700 $aMontesinos Lo?pez$b Osval Antonio$00 701 $aMontesinos Lo?pez$b Abelardo$00 701 $aCrossa$b Jose?$00 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522999103321 996 $aMultivariate Statistical Machine Learning Methods for Genomic Prediction$92590779 997 $aUNINA