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	Provedor de dados Scientia Agricola (2) Acta Agron. (Palmira) (1) Autor Piepho,Hans-Peter (2) Silva,Fabyano Fonseca e (2) Viana,José Marcelo Soriano (2) Ganganagowder,Narendra Veernagouda (1) Kamath,Priya (1) Palavra-chave Prediction accuracy (3) Genome-wide selection (2) Additive value prediction (1) Algorithm (1) Digital images (1) Dominance value prediction (1) Food classifiers (1) Training/test (1) Mais... Tipo do documento Info:eu-repo/semantics/article (3) Ano 2017 (2) 2016 (1) País Brazil (2) Colombia (1) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations Scientia Agricola Viana,José Marcelo Soriano; Piepho,Hans-Peter; Silva,Fabyano Fonseca e. ABSTRACT: Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL... Tipo: Info:eu-repo/semantics/article Palavras-chave: Genome-wide selection; Dominance value prediction; Prediction accuracy. Ano: 2017 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000100041 Quantitative genetics theory for genomic selection and efficiency of breeding value prediction in open-pollinated populations Scientia Agricola Viana,José Marcelo Soriano; Piepho,Hans-Peter; Silva,Fabyano Fonseca e. ABSTRACT To date, the quantitative genetics theory for genomic selection has focused mainly on the relationship between marker and additive variances assuming one marker and one quantitative trait locus (QTL). This study extends the quantitative genetics theory to genomic selection in order to prove that prediction of breeding values based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance. We also assessed the efficiency of genomic selection in relation to phenotypic selection, assuming mass selection in an open-pollinated population, all QTLs of lower effect, and reduced sample size, based on simulated data. We show that the average effect of a SNP substitution is... Tipo: Info:eu-repo/semantics/article Palavras-chave: Genome-wide selection; Additive value prediction; Prediction accuracy. Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300243 Intelligent classification models for food products basis on morphological, colour and texture features Acta Agron. (Palmira) Ganganagowder,Narendra Veernagouda; Kamath,Priya. Abstract The aim of this research is to build a supervised intelligent classification model of food products such as Biscuits, Cereals, Vegetables, Edible nuts and etc., using digital images. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. The best prediction accuracy is obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). The percentage of correctly classified instances is very high in these... Tipo: Info:eu-repo/semantics/article Palavras-chave: Algorithm; Digital images; Food classifiers; Prediction accuracy; Training/test. Ano: 2017 URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-28122017000400486 Registros recuperados: 3 Primeira ... 1 ... Última

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