A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

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Title:: A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species
Document type:: Zeitschriftenaufsatz
Author(s):: John, Maura ; Haselbeck, Florian ; Dass, Rupashree ; Malisi, Christoph ; Ricca, Patrizia ; Dreischer, Christian ; Schultheiss, Sebastian J. ; Grimm, Dominik G.
Keywords:: Plant Science ; phenotype prediction ; genomic selection ; plant phenotyping ; machine learning ;
Journal title:: Frontiers in Plant Science
Year:: 2022
Journal volume:: 13
Fulltext / DOI:: doi:10.3389/fpls.2022.932512
Publisher:: Frontiers Media S.A.
E-ISSN:: 1664-462X
Date of publication:: 04.11.2022
CC license:: by, https://creativecommons.org/licenses/by/4.0

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