Tuning Matters: Comparing Lambda Optimization Approaches for Ridge Regression in Genomic Prediction
This study contributes valuable insights into optimizing hyperparameter selection for high-dimensional prediction problems, with direct implications for genomic selection and other applications in the life sciences.
Authors
- José Cricelio Montesinos-López
- Réka Howard (ORCID: https://orcid.org/0000-0002-8837-3770)
- Osval Antonio Montesinos-López (ORCID: https://orcid.org/0000-0002-3973-6547)
- Federico Lecumberry (ORCID: https://orcid.org/0000-0002-5491-2019)
- José Crossa (ORCID: https://orcid.org/0000-0001-9429-5855)
- María Inés Fariello (ORCID: https://orcid.org/0000-0002-3337-1209)
- Juan Manuel Ramirez Alcaraz
- Abelardo Montesinos-López
- Eduardo A. Barajas‐Ramirez
Institutions
- University of Nebraska–Lincoln (US)
- Universidad de la República de Uruguay (UY)
- Universidad de Guadalajara (MX)
- Centro Internacional de Mejoramiento de Maíz Y Trigo (MX)
- Colegio de Postgraduados (MX)
- University of California, Davis (US)
- Universidad de Colima (MX)
Publication Details
- Journal
- Genes
- Published
- 2025-05-23
- DOI
- https://doi.org/10.3390/genes16060618
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00