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Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

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dc.contributor.author Sousa, Ithalo Coelho de
dc.contributor.author Nascimento, Moysés
dc.contributor.author Silva, Gabi Nunes
dc.contributor.author Nascimento, Ana Carolina Campana
dc.contributor.author Cruz, Cosme Damião
dc.contributor.author Silva, Fabyano Fonseca e
dc.contributor.author Almeida, Dênia Pires de
dc.contributor.author Pestana, Kátia Nogueira
dc.contributor.author Azevedo, Camila Ferreira
dc.contributor.author Zambolim, Laércio
dc.contributor.author Caixeta, Eveline Teixeira
dc.date.accessioned 2022-01-26T16:25:19Z
dc.date.available 2022-01-26T16:25:19Z
dc.date.issued 2021
dc.identifier.citation SOUSA, I. C. et al. Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. Scientia Agrícola, Piracicaba, v. 78, n. 4, p. 1-8, 2021. pt_BR
dc.identifier.issn 1678-992X
dc.identifier.uri http://dx.doi.org/10.1590/1678-992X-2020-0021 pt_BR
dc.identifier.uri http://www.sbicafe.ufv.br/handle/123456789/13243
dc.description.abstract Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature. pt_BR
dc.format pdf pt_BR
dc.language.iso en pt_BR
dc.publisher Escola Superior de Agricultura "Luiz de Queiroz" pt_BR
dc.relation.ispartofseries Scientia Agrícola;v.78, n.4, 2021
dc.rights Open Access pt_BR
dc.subject Hemileia vastatrix pt_BR
dc.subject Statistical learning pt_BR
dc.subject Plant breeding pt_BR
dc.subject Artificial intelligence pt_BR
dc.subject.classification Cafeicultura::Genética e melhoramento pt_BR
dc.title Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms pt_BR
dc.type Artigo pt_BR

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