Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids (2022)
- Authors:
- USP affiliated authors: FRITSCHE NETO, ROBERTO - ESALQ ; GALLI, GIOVANNI - ESALQ ; YASSUE, RAFAEL MASSAHIRO - ESALQ
- Unidade: ESALQ
- DOI: 10.3389/fpls.2022.845524
- Subjects: APRENDIZADO COMPUTACIONAL; GENÔMICA; HIBRIDAÇÃO VEGETAL; MILHO; REDES NEURAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Frontiers in Plant Science
- ISSN: 1664-462X
- Volume/Número/Paginação/Ano: v. 13, art. 845524, p. 1-13, March 2022
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
GALLI, Giovanni et al. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids. Frontiers in Plant Science, v. 13, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.3389/fpls.2022.845524. Acesso em: 19 set. 2024. -
APA
Galli, G., Sabadin, F., Yassue, R. M., Galves, C., Carvalho, H. F., Crossa, J., et al. (2022). Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids. Frontiers in Plant Science, 13, 1-13. doi:10.3389/fpls.2022.845524 -
NLM
Galli G, Sabadin F, Yassue RM, Galves C, Carvalho HF, Crossa J, Montesinos-López OA, Fritsche-Neto R. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids [Internet]. Frontiers in Plant Science. 2022 ; 13 1-13.[citado 2024 set. 19 ] Available from: https://doi.org/10.3389/fpls.2022.845524 -
Vancouver
Galli G, Sabadin F, Yassue RM, Galves C, Carvalho HF, Crossa J, Montesinos-López OA, Fritsche-Neto R. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids [Internet]. Frontiers in Plant Science. 2022 ; 13 1-13.[citado 2024 set. 19 ] Available from: https://doi.org/10.3389/fpls.2022.845524 - CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction
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Informações sobre o DOI: 10.3389/fpls.2022.845524 (Fonte: oaDOI API)
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