Ejemplares similares: Machine learning algorithms translate big data into predictive breeding accuracy
- Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize
- Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
- A marker weighting approach for enhancing within-family accuracy in genomic prediction
- Improving wheat grain yield genomic prediction accuracy using historical data
- Deep learning methods improve genomic prediction of wheat breeding
- Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data
Autor: Crossa, José
- Near-infrared spectroscopy to predict provitamin A carotenoids content in maize
- EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
- Deep learning methods improve genomic prediction of wheat breeding
- Status of implementation new tools and technologies in the GMP- EA-PP1 Africa breeding pipeline
- Author Correction: Enhanced radiation use efficiency and grain filling rate as the main drivers of grain yield genetic gains in the CIMMYT elite spring wheat yield trial
- Genetic analyses of tropical maize lines under artificial infestation of fall armyworm and foliar diseases under optimum conditions
Autor: Montesinos-Lopez, Osval A.
- Enhancing wheat genomic prediction by a hybrid kernel approach
- Data augmentation enhances plant-genomic-enabled predictions
- A marker weighting approach for enhancing within-family accuracy in genomic prediction
- Optimizing genomic prediction with transfer learning under a ridge regression framework
- Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities
- Genotype performance estimation in targeted production environments by using sparse genomic prediction