Genome-enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods

Keyphrases

• Reproductive Traits 100%
• Machine Learning Techniques 100%
• Least Squares Support Vector Regression (LSSVR) 100%
• Early Pregnancy 100%
• Scrotal Circumference 100%
• Genomic Prediction 100%
• Nellore Cattle 100%
• Stayability 100%
• Parametric Model Learning 100%
• First Calving 80%
• Predictive Ability 60%
• Parametric Model 60%
• Genomic Best Linear Unbiased Prediction (GBLUP) 60%
• BLASSO 60%
• Prediction Accuracy 40%
• Random Forest 40%
• Bayesian Regularized Artificial Neural Network 40%
• Linear Correlation 20%
• Mean Squared Error 20%
• Square Root 20%
• Heritability 20%
• Suitable Method 20%
• Low-to-moderate 20%
• Training Phase 20%
• Breeding Value 20%
• Least Absolute Shrinkage and Selection Operator (LASSO) 20%
• Best Linear Unbiased Prediction 20%
• Validation Strategies 20%
• SNP Markers 20%
• Hyperparameters 20%
• Bandwidth Parameter 20%
• Kernel Bandwidth 20%
• Categorical Trait 20%
• 5-fold Cross Validation 20%
• Continuous Traits 20%
• Age at First Calving 20%

Biochemistry, Genetics and Molecular Biology

• Support Vector Machine 100%
• First Trimester Pregnancy 100%
• Nellore Cattle 100%
• Artificial Neural Network 40%
• Random Forest 40%
• Genomics 40%
• Single Nucleotide Polymorphism 20%

Agricultural and Biological Sciences

• Reproductive Traits 100%
• Support Vector Machine 100%
• Nellore 100%
• Neural Network 40%
• Single Nucleotide Polymorphism 20%
• Heritability 20%
• Breeding Value 20%