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STPGA: Selection of training populations with a genetic algorithm
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Outgoing claims: the papers this work cites, builds on, or argues with.
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The Arrangement of Field Experiments
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Training set optimization under population structure in genomic selection
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Genomic selection: prediction of accuracy and maximisation of long term response
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A proposal of criteria of robustness analysis in genetic evaluation
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Genetic Algorithms and Machine Learning
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Quantum computing in molecular magnets
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Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
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Common SNPs explain a large proportion of the heritability for human height
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Genome-wide association study of leaf architecture in the maize nested association mapping population
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Genomic prediction contributing to a promising global strategy to turbocharge gene banks
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Precision and information in linear models of genetic evaluation
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Precision and information in linear models of genetic evaluation
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Branch-and-Bound Search for Experimental Designs Based onDOptimality and Other Criteria
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The Application of the Annealing Algorithm to the Construction of Exact Optimal Designs for Linear – Regression Models
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A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
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Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
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The Pfam protein families database: towards a more sustainable future
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Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
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Optimum Experimental Designs
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Regression Shrinkage and Selection Via the Lasso
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GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment
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The Future of Genetic Studies of Complex Human Diseases
inferredcites →https://doi.org/10.1137/1.9780898719109openalex-snapshot
Optimal Design of Experiments
inferredcites →https://doi.org/10.1137/1.9781611970128openalex-snapshot
Spline Models for Observational Data
inferredcites →https://doi.org/10.1145/1465482.1465560openalex-snapshot
Validity of the single processor approach to achieving large scale computing capabilities
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Genetic algorithms and machine learning
inferredcites →https://doi.org/10.1186/1297-9686-42-35openalex-snapshot
Dynamics of long-term genomic selection
inferredcites →https://doi.org/10.1186/s12711-015-0116-6openalex-snapshot
Optimization of genomic selection training populations with a genetic algorithm
inferredcites →https://doi.org/10.1214/aos/1176344136openalex-snapshot
Estimating the Dimension of a Model
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Beyond Missing Heritability: Prediction of Complex Traits
inferredcites →https://doi.org/10.1534/g3.116.029637openalex-snapshot
Genomic Prediction of Gene Bank Wheat Landraces
inferredcites →https://doi.org/10.1534/genetics.105.049510openalex-snapshot
Genomic-Assisted Prediction of Genetic Value With Semiparametric Procedures
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Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers
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Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers
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Maximizing the Reliability of Genomic Selection by Optimizing the Calibration Set of Reference Individuals: Comparison of Methods in Two Diverse Groups of Maize Inbreds (Zea maysL.)
inferredcites →https://doi.org/10.1534/genetics.112.147983openalex-snapshot
Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking
inferredcites →https://doi.org/10.1534/genetics.114.173658openalex-snapshot
Locally Epistatic Genomic Relationship Matrices for Genomic Association and Prediction
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GA: A Package for Genetic Algorithms inR
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A Distributed Procedure for Computing Stochastic Expansions withMathematica
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Branch-and-Bound Search for Experimental Designs Based on D Optimality and Other Criteria
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An Algorithm for the Construction of "D-Optimal" Experimental Designs
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An Algorithm for the Construction of "D-Optimal" Experimental Designs
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Jack Carl Kiefer Collected Papers III: Design of Experiments.
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Spline Models for Observational Data.
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The Estimation of Environmental and Genetic Trends from Records Subject to Culling
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Best Linear Unbiased Estimation and Prediction under a Selection Model
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Estimation of Variance and Covariance Components
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Maximizing the response of selection with a predefined rate of inbreeding.
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Efficient Methods to Compute Genomic Predictions
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Invited Review: Reliability of genomic predictions for North American Holstein bulls
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Invited review: Genomic selection in dairy cattle: Progress and challenges
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Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information
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Controlling inbreeding and maximizing genetic gain using semi-definite programming with pedigree-based and genomic relationships
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Mating programs including genomic relationships and dominance effects
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EMMREML: Fitting Mixed Models with Known Covariance Structures
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gafit: Genetic Algorithm for Curve Fitting
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galts: Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares) Estimation
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quadprog: Functions to Solve Quadratic Programming Problems
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Adaptation in Natural and Artificial Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs
inferred← citeshttps://doi.org/10.1002/tpg2.20286openalex-snapshot
Genomic prediction of tocochromanols in exotic‐derived maize
inferred← citeshttps://doi.org/10.1002/tpg2.20559openalex-snapshot
Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs
inferred← citeshttps://doi.org/10.1002/tpg2.70047openalex-snapshot
Genomic prediction in Persian walnut: Optimization levers according to genetic architecture of complex traits
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GS4PB: An R Shiny application to facilitate a genomic selection pipeline for plant breeding
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Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches
inferred← citeshttps://doi.org/10.1007/s00122-018-3068-8openalex-snapshot
Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs
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Optimization of training sets for genomic prediction of early-stage single crosses in maize
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Genomic prediction and training set optimization in a structured Mediterranean oat population
inferred← citeshttps://doi.org/10.1007/s00122-022-04236-3openalex-snapshot
Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops
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A comparison of methods for training population optimization in genomic selection
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Optimization of sparse phenotyping strategy in multi-environmental trials in maize
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Optimization of Selective Phenotyping and Population Design for Genomic Prediction
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Genomic selection in plant breeding: Key factors shaping two decades of progress
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Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple
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Design of training populations for selective phenotyping in genomic prediction
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The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years
inferred← citeshttps://doi.org/10.1101/2021.03.05.434084openalex-snapshot
Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops
inferred← citeshttps://doi.org/10.1101/2021.06.11.448049openalex-snapshot
Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize
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Optimization of sampling designs for pedigrees and association studies
inferred← citeshttps://doi.org/10.1111/pbr.12827openalex-snapshot
Using public databases for genomic prediction of tropical maize lines
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Evaluation of strategies to optimize training populations for genomic prediction in oat ( Avena sativa )
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Designing optimal training sets for genomic prediction using adversarial validation with probit regression
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Enviromic-based kernels optimize resource allocation with multi-trait multi-environment genomic prediction for tropical maize
inferred← citeshttps://doi.org/10.1186/s12870-022-03975-1openalex-snapshot
Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1005929/v1openalex-snapshot
Genotyping marker density reduction is not an effective approach in long-term prediction-based breeding of cross-pollinated crops
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1329691/v1openalex-snapshot
Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4355565/v1openalex-snapshot
Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops
inferred← citeshttps://doi.org/10.3389/fgene.2022.964684openalex-snapshot
Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat
inferred← citeshttps://doi.org/10.3389/fpls.2021.715910openalex-snapshot
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview
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Optimizing core collections for genetic studies: a worldwide flax germplasm case study
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