Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. - Wikidata - awgldk - TriplyDB

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

http://www.wikidata.org/entity/Q42395288

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Wheat quality improvement at CIMMYT and the use of genomic selection on it The contribution of dominance to phenotype prediction in a pine breeding and simulated population Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data.Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement.Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.Genomic Prediction of Gene Bank Wheat Landraces Genomic-enabled prediction with classification algorithms.Bayesian genomic-enabled prediction as an inverse problem Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines Integrated genomic and BMI analysis for type 2 diabetes risk assessment.Upweighting rare favourable alleles increases long-term genetic gain in genomic selection programs Genome wide analysis of flowering time trait in multiple environments via high-throughput genotyping technique in Brassica napus L A Ranking Approach to Genomic Selection.Genome-enabled prediction using probabilistic neural network classifiers Incorporating parent-of-origin effects in whole-genome prediction of complex traits.Multikernel linear mixed models for complex phenotype prediction.Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.).Genomic prediction in maize breeding populations with genotyping-by-sequencing Genomic prediction in CIMMYT maize and wheat breeding programs.Allele frequency changes due to hitch-hiking in genomic selection programs.Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation.Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits.Kernel-based whole-genome prediction of complex traits: a review Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.A Genomic Bayesian Multi-trait and Multi-environment Model.Genomic prediction for grain zinc and iron concentrations in spring wheat.Modeling Epistasis in Genomic Selection.Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat.Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding.Genome-wide prediction of traits with different genetic architecture through efficient variable selection.Assessing Predictive Properties of Genome-Wide Selection in Soybeans.Genome-enabled methods for predicting litter size in pigs: a comparison.Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield.Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates.Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat.

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P2860

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

http://www.wikidata.org/entity/Q42395288

description

2012 nî lūn-bûn

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2012年の論文

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2012年論文

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2012年論文

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name

Comparison between linear and ...... e-enabled prediction in wheat.

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Comparison between linear and ...... e-enabled prediction in wheat.

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type

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Comparison between linear and ...... e-enabled prediction in wheat.

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Comparison between linear and ...... e-enabled prediction in wheat.

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Comparison between linear and ...... e-enabled prediction in wheat.

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Comparison between linear and ...... e-enabled prediction in wheat.

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Comparison between linear and ...... e-enabled prediction in wheat.

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Daniel Gianola

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Juan Manuel González-Camacho

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Yann Manès

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P2860

Prediction of total genetic value using genome-wide dense marker maps

Beyond missing heritability: prediction of complex traits

Extension of the bayesian alphabet for genomic selection.

Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat.

Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).

Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

Bayesian approach for neural networks--review and case studies.

Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster.

Genomic-assisted prediction of genetic value with semiparametric procedures.

Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R.

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10.1534/G3.112.003665

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12

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2

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Paulino Pérez-Rodríguez

Susanne Dreisigacker

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2012-12-01T00:00:00Z

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234012489

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23275882

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