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Article

MGIDI: toward an effective multivariate selection in biological experiments.

Bioinformatics (Oxford, England) - 16 Jun 2021

Olivoto Tiago, Nardino Maicon

Abstract excerpt

MOTIVATION: Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of...

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PMID: 33226063
DOI: 10.1093/bioinformatics/btaa981

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