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

Benutzer: Gast

Dokumenttyp:: Zeitschriftenaufsatz
Autor(en):: John, Maura ; Haselbeck, Florian ; Dass, Rupashree ; Malisi, Christoph ; Ricca, Patrizia ; Dreischer, Christian ; Schultheiss, Sebastian J. ; Grimm, Dominik G.
Titel:: A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species
Stichworte:: Plant Science ; phenotype prediction ; genomic selection ; plant phenotyping ; machine learning ;
Zeitschriftentitel:: Frontiers in Plant Science
Jahr:: 2022
Band / Volume:: 13
Volltext / DOI:: doi:10.3389/fpls.2022.932512
Verlag / Institution:: Frontiers Media S.A.
E-ISSN:: 1664-462X
Publikationsdatum:: 04.11.2022
CC-Lizenz:: by, https://creativecommons.org/licenses/by/4.0

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