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...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cham, Switzerland :
Springer
2022.
|
| Colección: | Springer open access eBooks.
|
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b47264354*spi |
Tabla de Contenidos:
- Preface
- 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.
