Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa

Keyphrases

• Machine Learning 100%
• Alfalfa 100%
• Multi-trait 100%
• New Markers 100%
• Stem Traits 100%
• Modelling Learning 100%
• Trait Modeling 100%
• Genome-wide Association Study 80%
• Molecular Markers 60%
• Support Vector Machine 60%
• Winter Damage 60%
• Stem pith 60%
• Linear Mixed Model 40%
• Solid Stem 40%
• Standing Ability 40%
• Nutritional Value 20%
• Genetic Structure 20%
• Breeding Program 20%
• High-performance Fibers 20%
• Marker-assisted Selection 20%
• Related Traits 20%
• Palatability 20%
• Winter Hardiness 20%
• Coding Region 20%
• SNP Markers 20%
• Phenotypic Traits 20%
• Divergent Selection 20%
• Morphological Traits 20%
• Component Structure 20%
• Response Variable 20%
• Random Forest 20%
• Nutritional Intake 20%
• Protein-rich 20%
• Random Forest Algorithm 20%
• Ruminant Animals 20%
• Population Structure Analysis 20%
• 2-cycles 20%
• Additional Markers 20%
• Stem Colour 20%
• In Vitro Neutral Detergent Fiber Digestibility 20%

Agricultural and Biological Sciences

• Medicago sativa 100%
• Genome-Wide Association Study 80%
• Pith 60%
• Support Vector Machine 60%
• Digestibility 60%
• Linear Models 40%
• Marker-Assisted Selection 20%
• Winter Hardiness 20%
• Palatability 20%
• Single Nucleotide Polymorphism 20%
• Neutral Detergent Fiber 20%
• Nutritive Value 20%
• Genetic Structure 20%
• Population Structure 20%