التفاصيل البيبلوغرافية
| العنوان: |
A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets. |
| المؤلفون: |
Montesinos-López, Osval A., Pulido-Carrillo, Cristian Daniel, Montesinos-López, Abelardo, Larios Trejo, Jesús Antonio, Montesinos-López, José Cricelio, Agbona, Afolabi, Crossa, José |
| المصدر: |
Genes; Aug2024, Vol. 15 Issue 8, p969, 17p |
| مصطلحات موضوعية: |
STANDARD deviations, WEIGHT training, PLANT breeding, ABSOLUTE value, REGRESSION analysis |
| مستخلص: |
Genomic selection (GS) is changing plant breeding by significantly reducing the resources needed for phenotyping. However, its accuracy can be compromised by mismatches between training and testing sets, which impact efficiency when the predictive model does not adequately reflect the genetic and environmental conditions of the target population. To address this challenge, this study introduces a straightforward method using binary-Lasso regression to estimate β coefficients. In this approach, the response variable assigns 1 to testing set inputs and 0 to training set inputs. Subsequently, Lasso, Ridge, and Elastic Net regression models use the inverse of these β coefficients (in absolute values) as weights during training (WLasso, WRidge, and WElastic Net). This weighting method gives less importance to features that discriminate more between training and testing sets. The effectiveness of this method is evaluated across six datasets, demonstrating consistent improvements in terms of the normalized root mean square error. Importantly, the model's implementation is facilitated using the glmnet library, which supports straightforward integration for weighting β coefficients. [ABSTRACT FROM AUTHOR] |
|
Copyright of Genes is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder