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Genotype prediction in maize (Zea mays L.) progeny using different predictive models

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dc.contributor.authorPolyvanyi, A.
dc.contributor.authorButenko, A.
dc.contributor.authorMikulina, M.
dc.contributor.authorZubko, V.
dc.contributor.authorKharchenko, S.
dc.contributor.authorDubovyk, V.
dc.contributor.authorDubovyk, O.
dc.contributor.authorSarzhanov, B.
dc.date.accessioned2024-08-13T09:38:29Z
dc.date.available2024-08-13T09:38:29Z
dc.date.issued2024
dc.descriptionReceived: March 29th, 2024 ; Accepted: June 27th, 2024 ; Published: July 11th, 2024 ; Correspondence: polivanui1@gmail.comeng
dc.description.abstractThis 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.eng
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2024, vol. 22, no. 2, pp. 887–897eng
dc.identifier.urihttp://hdl.handle.net/10492/9443
dc.identifier.urihttps://doi.org/10.15159/ar.24.063
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)eng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectGaussian Naive Bayes (GNB)eng
dc.subjectgenotype predictioneng
dc.subjectLogistic Regression (LR)eng
dc.subjectpredictive modelseng
dc.subjectZea mays L.lat
dc.subjectarticleseng
dc.titleGenotype prediction in maize (Zea mays L.) progeny using different predictive modelseng
dc.typeinfo:eu-repo/semantics/articleeng

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2024, Vol. 22, No. 2