LEADER 03216nam 22006373 450 001 9910522999103321 005 20220207155350.0 010 $a3-030-89010-4 035 $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(EXLCZ)995100000000193939 100 $a20220207d2022 uy 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 210 $aCham$cSpringer Nature$d2022 210 1$aCham :$cSpringer International Publishing AG,$d2022. 210 4$d©2022. 215 $a1 online resource (707 pages) 311 $a3-030-89009-0 330 $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 $aAgricultural science$2bicssc 606 $aLife sciences: general issues$2bicssc 606 $aBotany & plant sciences$2bicssc 606 $aAnimal reproduction$2bicssc 606 $aProbability & statistics$2bicssc 610 $aopen access 610 $aStatistical learning 610 $aBayesian regression 610 $aDeep learning 610 $aNon linear regression 610 $aPlant breeding 610 $aCrop management 610 $amulti-trait multi-environments models 615 7$aAgricultural science 615 7$aLife sciences: general issues 615 7$aBotany & plant sciences 615 7$aAnimal reproduction 615 7$aProbability & statistics 700 $aMontesinos López$b Osval Antonio$01078558 701 $aMontesinos López$b Abelardo$01078559 701 $aCrossa$b José$01078560 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522999103321 996 $aMultivariate Statistical Machine Learning Methods for Genomic Prediction$92590779 997 $aUNINA LEADER 02722nas 2200541 n 450 001 990008882910403321 005 20240229084618.0 011 $a0001-3218 035 $a000888291 035 $aFED01000888291 035 $a(Aleph)000888291FED01 035 $a000888291 091 $2CNR$aP 00000006 100 $a20090724a19629999km-y0itaa50------ba 101 0 $aita 102 $aIT 110 $aauu-------- 200 1 $aAbitare 207 1$a1962- 210 $aMilano$cEditrice Segesta 326 $aMensile 421 0$1001000900736$12001$aMettiamo su casa insieme 421 0$12001$aProdurre per Abitare 430 0$12001$aCasa novità 530 0 $aAbitare 675 $a72 675 $a643 676 $a747 801 0$aIT$bACNP$c20090723 859 4 $uhttp://acnp.cib.unibo.it/cgi-ser/start/it/cnr/dc-p1.tcl?catno=4&person=false&language=ITALIANO&libr=&libr_th=unina1$zBiblioteche che possiedono il periodico 901 $aSE 912 $a990008882910403321 958 $aBiblioteca. 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