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Linear Selection Indices in Modern Plant Breeding [[electronic resource] /] / by J. Jesus Céron-Rojas, José Crossa
| Linear Selection Indices in Modern Plant Breeding [[electronic resource] /] / by J. Jesus Céron-Rojas, José Crossa |
| Autore | Céron-Rojas J. Jesus |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2018 |
| Descrizione fisica | 1 online resource (XXII, 256 p. 45 illus. in color.) |
| Disciplina | 570.15195 |
| Soggetto topico |
Biostatistics
Plant breeding Animal genetics Plant Breeding/Biotechnology Animal Genetics and Genomics |
| Soggetto non controllato |
Life sciences
Biostatistics Plant breeding Animal genetics |
| ISBN | 3-319-91223-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | General introduction -- The linear phenotypic selection index theory -- Constrained linear phenotypic selection indices -- Constrained linear phenotypic selection indices -- Linear marker and genomic selection indices -- Linear genomic selection indices -- Constrained linear genomic selection indices -- Linear phenotypic eigen selection index methods -- Linear molecular and genomic eigen selection index methods -- Multistage linear selection indices -- Stochastic simulation of four linear phenotypic selection indices -- RIndSel: Selection indices with R. |
| Record Nr. | UNINA-9910298406803321 |
Céron-Rojas J. Jesus
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| Cham, : Springer Nature, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multivariate Statistical Machine Learning Methods for Genomic Prediction
| Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| Autore | Montesinos López Osval Antonio |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (707 pages) |
| Altri autori (Persone) |
Montesinos LópezAbelardo
CrossaJosé |
| Soggetto topico |
Agricultural science
Life sciences: general issues Botany & plant sciences Animal reproduction Probability & statistics Aprenentatge automàtic Genètica vegetal Estadística matemàtica Anàlisi multivariable Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
open access
Statistical learning Bayesian regression Deep learning Non linear regression Plant breeding Crop management multi-trait multi-environments models |
| ISBN | 3-030-89010-4 |
| Classificazione | MED090000SCI011000SCI070000SCI086000TEC003000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Chapter 1: General Elements of Genomic Selection and Statistical Learning -- 1.1 Data as a Powerful Weapon -- 1.2 Genomic Selection -- 1.2.1 Concepts of Genomic Selection -- 1.2.2 Why Is Statistical Machine Learning a Key Element of Genomic Selection? -- 1.3 Modeling Basics -- 1.3.1 What Is a Statistical Machine Learning Model? -- 1.3.2 The Two Cultures of Model Building: Prediction Versus Inference -- 1.3.3 Types of Statistical Machine Learning Models and Model Effects -- 1.3.3.1 Types of Statistical Machine Learning Models -- 1.3.3.2 Model Effects -- 1.4 Matrix Algebra Review -- 1.5 Statistical Data Types -- 1.5.1 Data Types -- 1.5.2 Multivariate Data Types -- 1.6 Types of Learning -- 1.6.1 Definition and Examples of Supervised Learning -- 1.6.2 Definitions and Examples of Unsupervised Learning -- 1.6.3 Definition and Examples of Semi-Supervised Learning -- References -- Chapter 2: Preprocessing Tools for Data Preparation -- 2.1 Fixed or Random Effects -- 2.2 BLUEs and BLUPs -- 2.3 Marker Depuration -- 2.4 Methods to Compute the Genomic Relationship Matrix -- 2.5 Genomic Breeding Values and Their Estimation -- 2.6 Normalization Methods -- 2.7 General Suggestions for Removing or Adding Inputs -- 2.8 Principal Component Analysis as a Compression Method -- Appendix 1 -- Appendix 2 -- References -- Chapter 3: Elements for Building Supervised Statistical Machine Learning Models -- 3.1 Definition of a Linear Multiple Regression Model -- 3.2 Fitting a Linear Multiple Regression Model via the Ordinary Least Square (OLS) Method -- 3.3 Fitting the Linear Multiple Regression Model via the Maximum Likelihood (ML) Method -- 3.4 Fitting the Linear Multiple Regression Model via the Gradient Descent (GD) Method -- 3.5 Advantages and Disadvantages of Standard Linear Regression Models (OLS and MLR).
3.6 Regularized Linear Multiple Regression Model -- 3.6.1 Ridge Regression -- 3.6.2 Lasso Regression -- 3.7 Logistic Regression -- 3.7.1 Logistic Ridge Regression -- 3.7.2 Lasso Logistic Regression -- Appendix 1: R Code for Ridge Regression Used in Example 2 -- References -- Chapter 4: Overfitting, Model Tuning, and Evaluation of Prediction Performance -- 4.1 The Problem of Overfitting and Underfitting -- 4.2 The Trade-Off Between Prediction Accuracy and Model Interpretability -- 4.3 Cross-validation -- 4.3.1 The Single Hold-Out Set Approach -- 4.3.2 The k-Fold Cross-validation -- 4.3.3 The Leave-One-Out Cross-validation -- 4.3.4 The Leave-m-Out Cross-validation -- 4.3.5 Random Cross-validation -- 4.3.6 The Leave-One-Group-Out Cross-validation -- 4.3.7 Bootstrap Cross-validation -- 4.3.8 Incomplete Block Cross-validation -- 4.3.9 Random Cross-validation with Blocks -- 4.3.10 Other Options and General Comments on Cross-validation -- 4.4 Model Tuning -- 4.4.1 Why Is Model Tuning Important? -- 4.4.2 Methods for Hyperparameter Tuning (Grid Search, Random Search, etc.) -- 4.5 Metrics for the Evaluation of Prediction Performance -- 4.5.1 Quantitative Measures of Prediction Performance -- 4.5.2 Binary and Ordinal Measures of Prediction Performance -- 4.5.3 Count Measures of Prediction Performance -- References -- Chapter 5: Linear Mixed Models -- 5.1 General of Linear Mixed Models -- 5.2 Estimation of the Linear Mixed Model -- 5.2.1 Maximum Likelihood Estimation -- 5.2.1.1 EM Algorithm -- E Step -- M Step -- 5.2.1.2 REML -- 5.2.1.3 BLUPs -- 5.3 Linear Mixed Models in Genomic Prediction -- 5.4 Illustrative Examples of the Univariate LMM -- 5.5 Multi-trait Genomic Linear Mixed-Effects Models -- 5.6 Final Comments -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- Appendix 6 -- Appendix 7 -- References. Chapter 6: Bayesian Genomic Linear Regression -- 6.1 Bayes Theorem and Bayesian Linear Regression -- 6.2 Bayesian Genome-Based Ridge Regression -- 6.3 Bayesian GBLUP Genomic Model -- 6.4 Genomic-Enabled Prediction BayesA Model -- 6.5 Genomic-Enabled Prediction BayesB and BayesC Models -- 6.6 Genomic-Enabled Prediction Bayesian Lasso Model -- 6.7 Extended Predictor in Bayesian Genomic Regression Models -- 6.8 Bayesian Genomic Multi-trait Linear Regression Model -- 6.8.1 Genomic Multi-trait Linear Model -- 6.9 Bayesian Genomic Multi-trait and Multi-environment Model (BMTME) -- Appendix 1 -- Appendix 2: Setting Hyperparameters for the Prior Distributions of the BRR Model -- Appendix 3: R Code Example 1 -- Appendix 4: R Code Example 2 -- Appendix 5 -- R Code Example 3 -- R Code for Example 4 -- References -- Chapter 7: Bayesian and Classical Prediction Models for Categorical and Count Data -- 7.1 Introduction -- 7.2 Bayesian Ordinal Regression Model -- 7.2.1 Illustrative Examples -- 7.3 Ordinal Logistic Regression -- 7.4 Penalized Multinomial Logistic Regression -- 7.4.1 Illustrative Examples for Multinomial Penalized Logistic Regression -- 7.5 Penalized Poisson Regression -- 7.6 Final Comments -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 (Example 4) -- Appendix 5 -- Appendix 6 -- References -- Chapter 8: Reproducing Kernel Hilbert Spaces Regression and Classification Methods -- 8.1 The Reproducing Kernel Hilbert Spaces (RKHS) -- 8.2 Generalized Kernel Model -- 8.2.1 Parameter Estimation Under the Frequentist Paradigm -- 8.2.2 Kernels -- 8.2.3 Kernel Trick -- 8.2.4 Popular Kernel Functions -- 8.2.5 A Two Separate Step Process for Building Kernel Machines -- 8.3 Kernel Methods for Gaussian Response Variables -- 8.4 Kernel Methods for Binary Response Variables -- 8.5 Kernel Methods for Categorical Response Variables. 8.6 The Linear Mixed Model with Kernels -- 8.7 Hyperparameter Tuning for Building the Kernels -- 8.8 Bayesian Kernel Methods -- 8.8.1 Extended Predictor Under the Bayesian Kernel BLUP -- 8.8.2 Extended Predictor Under the Bayesian Kernel BLUP with a Binary Response Variable -- 8.8.3 Extended Predictor Under the Bayesian Kernel BLUP with a Categorical Response Variable -- 8.9 Multi-trait Bayesian Kernel -- 8.10 Kernel Compression Methods -- 8.10.1 Extended Predictor Under the Approximate Kernel Method -- 8.11 Final Comments -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- Appendix 6 -- Appendix 7 -- Appendix 8 -- Appendix 9 -- Appendix 10 -- Appendix 11 -- References -- Chapter 9: Support Vector Machines and Support Vector Regression -- 9.1 Introduction to Support Vector Machine -- 9.2 Hyperplane -- 9.3 Maximum Margin Classifier -- 9.3.1 Derivation of the Maximum Margin Classifier -- 9.3.2 Wolfe Dual -- 9.4 Derivation of the Support Vector Classifier -- 9.5 Support Vector Machine -- 9.5.1 One-Versus-One Classification -- 9.5.2 One-Versus-All Classification -- 9.6 Support Vector Regression -- Appendix 1 -- Appendix 2 -- Appendix 3 -- References -- Chapter 10: Fundamentals of Artificial Neural Networks and Deep Learning -- 10.1 The Inspiration for the Neural Network Model -- 10.2 The Building Blocks of Artificial Neural Networks -- 10.3 Activation Functions -- 10.3.1 Linear -- 10.3.2 Rectifier Linear Unit (ReLU) -- 10.3.3 Leaky ReLU -- 10.3.4 Sigmoid -- 10.3.5 Softmax -- 10.3.6 Tanh -- 10.4 The Universal Approximation Theorem -- 10.5 Artificial Neural Network Topologies -- 10.6 Successful Applications of ANN and DL -- 10.7 Loss Functions -- 10.7.1 Loss Functions for Continuous Outcomes -- 10.7.2 Loss Functions for Binary and Ordinal Outcomes -- 10.7.3 Regularized Loss Functions -- 10.7.4 Early Stopping Method of Training. 10.8 The King Algorithm for Training Artificial Neural Networks: Backpropagation -- 10.8.1 Backpropagation Algorithm: Online Version -- 10.8.1.1 Feedforward Part -- 10.8.1.2 Backpropagation Part -- 10.8.2 Illustrative Example 10.1: A Hand Computation -- 10.8.3 Illustrative Example 10.2-By Hand Computation -- References -- Chapter 11: Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes -- 11.1 Hyperparameters to Be Tuned in ANN and DL -- 11.1.1 Network Topology -- 11.1.2 Activation Functions -- 11.1.3 Loss Function -- 11.1.4 Number of Hidden Layers -- 11.1.5 Number of Neurons in Each Layer -- 11.1.6 Regularization Type -- 11.1.7 Learning Rate -- 11.1.8 Number of Epochs and Number of Batches -- 11.1.9 Normalization Scheme for Input Data -- 11.2 Popular DL Frameworks -- 11.3 Optimizers -- 11.4 Illustrative Examples -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- References -- Chapter 12: Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes -- 12.1 Training DNN with Binary Outcomes -- 12.2 Training DNN with Categorical (Ordinal) Outcomes -- 12.3 Training DNN with Count Outcomes -- 12.4 Training DNN with Multivariate Outcomes -- 12.4.1 DNN with Multivariate Continuous Outcomes -- 12.4.2 DNN with Multivariate Binary Outcomes -- 12.4.3 DNN with Multivariate Ordinal Outcomes -- 12.4.4 DNN with Multivariate Count Outcomes -- 12.4.5 DNN with Multivariate Mixed Outcomes -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- References -- Chapter 13: Convolutional Neural Networks -- 13.1 The Importance of Convolutional Neural Networks -- 13.2 Tensors -- 13.3 Convolution -- 13.4 Pooling -- 13.5 Convolutional Operation for 1D Tensor for Sequence Data -- 13.6 Motivation of CNN. 13.7 Why Are CNNs Preferred over Feedforward Deep Neural Networks for Processing Images?. |
| Record Nr. | UNINA-9910522999103321 |
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Montesinos López Osval Antonio
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Plant biotechnology [[electronic resource] ] : current and future applications of genetically modified crops / / edited by Nigel G. Halford
| Plant biotechnology [[electronic resource] ] : current and future applications of genetically modified crops / / edited by Nigel G. Halford |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : J. Wiley, c2006 |
| Descrizione fisica | 1 online resource (317 p.) |
| Disciplina | 631.5233 |
| Altri autori (Persone) | HalfordN. G (Nigel G.) |
| Soggetto topico | Plant biotechnology |
| Soggetto genere / forma | Electronic books. |
| Soggetto non controllato |
Plant culture
Agricultural science/research - general Plant breeding |
| ISBN |
1-280-44875-X
9786610448753 0-470-02183-7 0-470-02182-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From primitive selection to genetic modification, ten thousand years of plant breeding / Nigel G. Halford -- Crop biotechnology in the United States : experiences and impacts / Sujatha Sankula -- Development of biotech crops in China / Qingzhong Xue, Yuhua Zhang, and Xianyin Zhang -- Advances in transformation technologies / Huw D. Jones -- Enhanced nutritional value of food crops / Dietrich Rein and Karin Herbers -- The production of long-chain polyunsaturated fatty acids in transgenic plants / Louise V. Michaelson ... [et al.] -- The application of genetic engineering to the improvement of cereal grain quality / Peter R. Shewry -- Improvements in starch quality / Michael M. Burrell -- Production of vaccines in GM plants / Liz Nicholson, M. Carmen Cañizares, and George P. Lomonossoff -- Prospects for using genetic modification to engineer drought tolerance in crops / S.G. Mundree ... [et al.] -- Salt tolerance / Eduardo Blumwald and Anil Grover -- Engineering fungal resistance in crops / Maarten Stuiver -- Plant food allergens / E.N. Clare Mills, John A. Jenkins, and Peter R. Shewry -- Environmental impact and gene-flow / P.J.W. Lutman and K. Berry -- Risk assessment, regulation, and labeling / Nigel G. Halford. |
| Record Nr. | UNINA-9910143742703321 |
| Chichester, England ; ; Hoboken, NJ, : J. Wiley, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Plant biotechnology [[electronic resource] ] : current and future applications of genetically modified crops / / edited by Nigel G. Halford
| Plant biotechnology [[electronic resource] ] : current and future applications of genetically modified crops / / edited by Nigel G. Halford |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : J. Wiley, c2006 |
| Descrizione fisica | 1 online resource (317 p.) |
| Disciplina | 631.5233 |
| Altri autori (Persone) | HalfordN. G (Nigel G.) |
| Soggetto topico | Plant biotechnology |
| Soggetto non controllato |
Plant culture
Agricultural science/research - general Plant breeding |
| ISBN |
1-280-44875-X
9786610448753 0-470-02183-7 0-470-02182-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From primitive selection to genetic modification, ten thousand years of plant breeding / Nigel G. Halford -- Crop biotechnology in the United States : experiences and impacts / Sujatha Sankula -- Development of biotech crops in China / Qingzhong Xue, Yuhua Zhang, and Xianyin Zhang -- Advances in transformation technologies / Huw D. Jones -- Enhanced nutritional value of food crops / Dietrich Rein and Karin Herbers -- The production of long-chain polyunsaturated fatty acids in transgenic plants / Louise V. Michaelson ... [et al.] -- The application of genetic engineering to the improvement of cereal grain quality / Peter R. Shewry -- Improvements in starch quality / Michael M. Burrell -- Production of vaccines in GM plants / Liz Nicholson, M. Carmen Cañizares, and George P. Lomonossoff -- Prospects for using genetic modification to engineer drought tolerance in crops / S.G. Mundree ... [et al.] -- Salt tolerance / Eduardo Blumwald and Anil Grover -- Engineering fungal resistance in crops / Maarten Stuiver -- Plant food allergens / E.N. Clare Mills, John A. Jenkins, and Peter R. Shewry -- Environmental impact and gene-flow / P.J.W. Lutman and K. Berry -- Risk assessment, regulation, and labeling / Nigel G. Halford. |
| Record Nr. | UNINA-9910829881403321 |
| Chichester, England ; ; Hoboken, NJ, : J. Wiley, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The Potato Crop [[electronic resource] ] : Its Agricultural, Nutritional and Social Contribution to Humankind / / edited by Hugo Campos, Oscar Ortiz
| The Potato Crop [[electronic resource] ] : Its Agricultural, Nutritional and Social Contribution to Humankind / / edited by Hugo Campos, Oscar Ortiz |
| Autore | Campos Hugo |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (518) |
| Disciplina | 630 |
| Soggetto topico |
Agriculture
Nutrition Plant breeding Food—Biotechnology Agricultural economics Nutrition Plant Breeding/Biotechnology Food Science Agricultural Economics |
| Soggetto non controllato |
Life sciences
Agriculture Nutrition Plant breeding Food—Biotechnology Agricultural economics |
| ISBN | 3-030-28683-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Global Food Security, Contributions from Sustainable Potato Agri-Food Systems -- The Potato and its Contribution to the Human Diet -- Enhancing Value Chains through Collective Actions: Lessons From the Andes, Africa and Asia -- Ex situ Conservation of Potato [Solanum Section Petota (Solanaceae)] Genetic Resources in Genebanks -- The Genes and Genomes of the Potato -- Potato Breeding -- Genetics and Cytogenetics of Potato -- Insect Pests Affecting Potatoes in Tropical, Subtropical and Temperate Regions -- Fungal, Oomycete and Plasmodiophorid Diseases of Potato -- Bacterial Diseases of Potato -- Viral Diseases in Potato -- Potato Seed Systems -- Participatory Research (PR) at CIP with Potato Farming Systems in the Andes: Evolution and Prospects -- Gender Topics on Potato Research and Development -- Index. . |
| Record Nr. | UNINA-9910367257303321 |
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Campos Hugo
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| Cham, : Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Pre-Field Screening Protocols for Heat-Tolerant Mutants in Rice [[electronic resource] /] / by Fatma Sarsu, Abdelbagi M.A. Ghanim, Priyanka Das, Rajeev N. Bahuguna, Paul Mbogo Kusolwa, Muhammed Ashraf, Sneh L. Singla-Pareek, Ashwani Pareek, Brian P. Forster, Ivan Ingelbrecht
| Pre-Field Screening Protocols for Heat-Tolerant Mutants in Rice [[electronic resource] /] / by Fatma Sarsu, Abdelbagi M.A. Ghanim, Priyanka Das, Rajeev N. Bahuguna, Paul Mbogo Kusolwa, Muhammed Ashraf, Sneh L. Singla-Pareek, Ashwani Pareek, Brian P. Forster, Ivan Ingelbrecht |
| Autore | Sarsu Fatma |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2018 |
| Descrizione fisica | 1 online resource (XII, 39 p. 16 illus., 10 illus. in color.) |
| Disciplina |
631.52
660.6 |
| Soggetto topico |
Plant breeding
Agriculture Plant Breeding/Biotechnology |
| Soggetto non controllato |
Life sciences
Plant breeding Agriculture |
| ISBN | 3-319-77338-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | General Introduction -- Screening Protocols for Heat Tolerance in Rice at the Seedling and Reproductive Stages -- Validation of Screening Protocols for Heat Tolerance in Rice -- Conclusion -- References. |
| Record Nr. | UNINA-9910298401403321 |
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Sarsu Fatma
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| Cham, : Springer Nature, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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