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

John, Maura; Haselbeck, Florian; Dass, Rupashree; Malisi, Christoph; Ricca, Patrizia; Dreischer, Christian; Schultheiss, Sebastian J; Grimm, Dominik G

John, Maura; Haselbeck, Florian; Dass, Rupashree; Malisi, Christoph; Ricca, Patrizia; Dreischer, Christian; Schultheiss, Sebastian J; Grimm, Dominik G.

Afiliação

John M; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.
Haselbeck F; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany.
Dass R; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.
Malisi C; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany.
Ricca P; Computomics GmbH, Tübingen, Germany.
Dreischer C; Computomics GmbH, Tübingen, Germany.
Schultheiss SJ; Computomics GmbH, Tübingen, Germany.
Grimm DG; Computomics GmbH, Tübingen, Germany.

Front Plant Sci ; 13: 932512, 2022.

Article em En | MEDLINE | ID: mdl-36407627

Palavras-chave

Arabidopsis thaliana; genomic selection; machine learning; phenotype prediction; plant phenotyping

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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