Genome‐enabled prediction through machine learning methods considering different levels of trait complexity-Reference-Cited by-同舟云学术

Genome‐enabled prediction through machine learning methods considering different levels of trait complexity

Published:2021-04-14 Issue:3 Volume:61 Page:1890-1902
ISSN:0011-183X
Container-title:Crop Science
language:en
Short-container-title:Crop Science

Author:

Barbosa Ivan de Paiva¹^ORCID,da Silva Michele Jorge¹^ORCID,da Costa Weverton Gomes¹^ORCID,Castro Sant'Anna Isabela²,Nascimento Moysés³^ORCID,Cruz Cosme Damião¹

Affiliation:

1. Department of General Biology Bioinformatics Laboratory Federal University of Viçosa – UFV Viçosa MG Brazil

2. Rubber Tree and Agroforestry Center Agronomic Institute ‐ IAC Votuporana SP Brazil

3. Department of Statistics Laboratory of Computational Intelligence and Statistical Learning Federal University of Viçosa – UFV Viçosa MG Brazil

Publisher

Wiley

Subject

Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/csc2.20488

Reference33 articles.

1. Locally epistatic models for genome-wide prediction and association by importance sampling

2. Selective efficiency of genome-wide selection in Coffea canephora breeding

3. New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program

4. Supervised independent component analysis as an alternative method for genomic selection in pigs

5. No unbiased estimator of the variance of K‐fold cross‐validation;Bengio Y.;Journal of Machine Learning Research,2004

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