Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population
Authors
Keywords
-
Journal
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-05-09
DOI
10.1007/s13253-021-00454-2
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Optimization of Selective Phenotyping and Population Design for Genomic Prediction
- (2020) Nicolas Heslot et al. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
- Design of training populations for selective phenotyping in genomic prediction
- (2019) Deniz Akdemir et al. Scientific Reports
- Training set determination for genomic selection
- (2019) Jen-Hsiang Ou et al. THEORETICAL AND APPLIED GENETICS
- Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates
- (2017) Ryokei Tanaka et al. THEORETICAL AND APPLIED GENETICS
- Taking the Human Out of the Loop: A Review of Bayesian Optimization
- (2016) Bobak Shahriari et al. PROCEEDINGS OF THE IEEE
- Walking through the statistical black boxes of plant breeding
- (2016) Alencar Xavier et al. THEORETICAL AND APPLIED GENETICS
- Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding
- (2015) Robert J. Tempelman JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
- Correction: 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
- (2015) Jennifer Spindel et al. PLoS Genetics
- Genome-Wide Regression and Prediction with the BGLR Statistical Package
- (2014) P. Perez et al. GENETICS
- Feeding the future
- (2013) Susan McCouch et al. NATURE
- Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision
- (2013) Victor Picheny et al. TECHNOMETRICS
- 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 mays L.)
- (2012) R. Rincent et al. GENETICS
- Exploring and Exploiting Genetic Variation from Unadapted Sorghum Germplasm in a Breeding Program
- (2011) D. R. Jordan et al. CROP SCIENCE
- Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa
- (2011) Keyan Zhao et al. Nature Communications
- Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers
- (2010) J. Crossa et al. GENETICS
- Breeding Technologies to Increase Crop Production in a Changing World
- (2010) M. Tester et al. SCIENCE
- Tolerance intervals for unbalanced one-way random effects models with covariates and heterogeneous variances
- (2008) Tsai-Yu Lin et al. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
- The impact of genetic relationship information on genome-assisted breeding values
- (2007) D. Habier et al. GENETICS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started