Multivariate Statistical Machine Learning Methods for Genomic Prediction
This 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 req...
Gorde:
| Egile Nagusiak: | , , |
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| Formatua: | Online |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Springer Nature
2022
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| Gaiak: | |
| Sarrera elektronikoa: | ONIX_20220214_9783030890100_13 |
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Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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| _version_ | 1869515147951734784 |
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| author | Montesinos López, Osval Antonio Montesinos López, Abelardo Crossa, José |
| author_browse | Crossa, José Montesinos López, Abelardo Montesinos López, Osval Antonio |
| author_facet | Montesinos López, Osval Antonio Montesinos López, Abelardo Crossa, José |
| author_sort | Montesinos López, Osval Antonio |
| collection | Directory of Open Access Books |
| description | This 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. |
| format | Online |
| id | doab-20.500.12854ir-78249 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-782492025-07-30T11:56:38Z Multivariate Statistical Machine Learning Methods for Genomic Prediction Montesinos López, Osval Antonio Montesinos López, Abelardo Crossa, José open access Statistical learning Bayesian regression Deep learning Non linear regression Plant breeding Crop management multi-trait multi-environments models thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics This 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. 2022-02-15T04:01:04Z 2022-02-15T04:01:04Z 2022-02-14T21:18:12Z 2022 book ONIX_20220214_9783030890100_13 OCN: 1294307405 https://library.oapen.org/handle/20.500.12657/52837 9783030890100 https://directory.doabooks.org/handle/20.500.12854/78249 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/52837/1/978-3-030-89010-0.pdf https://library.oapen.org/bitstream/20.500.12657/52837/1/978-3-030-89010-0.pdf https://library.oapen.org/bitstream/20.500.12657/52837/1/978-3-030-89010-0.pdf Springer Nature Springer International Publishing 10.1007/978-3-030-89010-0 10.1007/978-3-030-89010-0 9fa3421d-f917-4153-b9ab-fc337c396b5a Bill and Melinda Gates Foundation 218ec580-e21b-49dd-92ef-e3cdeab38e7d 9783030890100 Springer International Publishing 691 Cham [grantnumber unknown] open access |
| spellingShingle | open access Statistical learning Bayesian regression Deep learning Non linear regression Plant breeding Crop management multi-trait multi-environments models thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics Montesinos López, Osval Antonio Montesinos López, Abelardo Crossa, José Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| title | Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| title_full | Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| title_fullStr | Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| title_full_unstemmed | Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| title_short | Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| title_sort | multivariate statistical machine learning methods for genomic prediction |
| topic | open access Statistical learning Bayesian regression Deep learning Non linear regression Plant breeding Crop management multi-trait multi-environments models thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| topic_facet | open access Statistical learning Bayesian regression Deep learning Non linear regression Plant breeding Crop management multi-trait multi-environments models thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming::TVB Agricultural science thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSV Zoology and animal sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| url | ONIX_20220214_9783030890100_13 |
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