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Multivariate Statistical Machine Learning Methods for Genomic Prediction

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Multivariate Statistical Machine Learning Methods for Genomic Prediction
Περιγραφή: This book is open access under a CC BY 4.0 licenseThis 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.
Συγγραφείς: Osval Antonio Montesinos López, Abelardo Montesinos López, José Crossa
Resource Type: eBook.
Θέματα: Genomics, Agriculture, Biometry, Plant genetics--Statistical methods, Multivariate analysis--Data processing, Machine learning
Categories: TECHNOLOGY & ENGINEERING / Agriculture / General, COMPUTERS / Data Science / Bioinformatics, MEDICAL / Biostatistics, SCIENCE / Life Sciences / Botany, SCIENCE / Life Sciences / Genetics & Genomics
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  Availability: 0
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  Data: This book is open access under a CC BY 4.0 licenseThis 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.
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RecordInfo BibRecord:
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      – Code: 581.350727
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Genomics
        Type: general
      – SubjectFull: Agriculture
        Type: general
      – SubjectFull: Biometry
        Type: general
      – SubjectFull: Plant genetics--Statistical methods
        Type: general
      – SubjectFull: Multivariate analysis--Data processing
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Multivariate Statistical Machine Learning Methods for Genomic Prediction
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Osval Antonio Montesinos López
      – PersonEntity:
          Name:
            NameFull: Abelardo Montesinos López
      – PersonEntity:
          Name:
            NameFull: José Crossa
      – PersonEntity:
          Name:
            NameFull: Osval Antonio Montesinos López
      – PersonEntity:
          Name:
            NameFull: Abelardo Montesinos López
      – PersonEntity:
          Name:
            NameFull: José Crossa
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2022
            – D: 09
              M: 08
              Type: profile
              Y: 2024
          Identifiers:
            – Type: isbn-print
              Value: 9783030890094
            – Type: isbn-electronic
              Value: 9783030890100
          Titles:
            – TitleFull: Multivariate Statistical Machine Learning Methods for Genomic Prediction
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