Genotype prediction in maize (Zea mays L.) progeny using different predictive models
Laen...
Kuupäev
2024
Kättesaadav alates
Autorid
Polyvanyi, A.
Butenko, A.
Mikulina, M.
Zubko, V.
Kharchenko, S.
Dubovyk, V.
Dubovyk, O.
Sarzhanov, B.
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Estonian University of Life Sciences
Abstrakt
This study utilized two probabilistic methods, Gaussian Naive Bayes (GNB) and Logistic Regression (LR), to predict the genotypes of the offspring of two maize varieties: SC604 and KSC707, based on the phenotypic traits of the parent plant. The predictive performance of both models was evaluated by measuring their overall accuracy and calculating the area under receiver operating characteristic curve (AUC). The overall accuracy of both models ranged from 80% to 89%. The AUC values for the LR models were 0.88 or higher, while the GNB models had AUC values of 0.83 or higher. These results indicated that both models were successful in predicting the genetic makeup of the progeny. Furthermore, it was observed that both models were more accurate in predicting the SC604 genotype, which was found to be more consistent and predictable compared to the KSC707 genotype. A chi-square test was conducted to assess the similarity between the prediction results of the two models, revealing that both models had a similarly high likelihood of making accurate predictions in all scenarios.
Kirjeldus
Received: March 29th, 2024 ; Accepted: June 27th, 2024 ; Published: July 11th, 2024 ; Correspondence: polivanui1@gmail.com
Märksõnad
Gaussian Naive Bayes (GNB), genotype prediction, Logistic Regression (LR), predictive models, Zea mays L., articles