ANOCVA in R: a software to compare clusters between groups and its application to the study of autism spectrum disorder (2017)
- Authors:
- USP affiliated authors: FUJITA, ANDRÉ - IME ; VIDAL, MACIEL CALEBE - IME
- Unidade: IME
- DOI: 10.3389/fnins.2017.00016
- Subjects: CIÊNCIA DA COMPUTAÇÃO; CIÊNCIA DA COMPUTAÇÃO; ESTATÍSTICA; ANÁLISE DE VARIÂNCIA
- Keywords: Analysis of Cluster Variability; silhouette statistic; functional brain network; ABIDE; fMRI
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Frontiers in Neuroscience
- ISSN: 1662-453X
- Volume/Número/Paginação/Ano: v.11, p. 1-8, 2017
- 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
VIDAL, Maciel Calebe et al. ANOCVA in R: a software to compare clusters between groups and its application to the study of autism spectrum disorder. Frontiers in Neuroscience, v. 11, p. 1-8, 2017Tradução . . Disponível em: https://doi.org/10.3389/fnins.2017.00016. Acesso em: 04 out. 2024. -
APA
Vidal, M. C., Sato, J. R., Balardin, J. B., Takahashi, D. Y., & Fujita, A. (2017). ANOCVA in R: a software to compare clusters between groups and its application to the study of autism spectrum disorder. Frontiers in Neuroscience, 11, 1-8. doi:10.3389/fnins.2017.00016 -
NLM
Vidal MC, Sato JR, Balardin JB, Takahashi DY, Fujita A. ANOCVA in R: a software to compare clusters between groups and its application to the study of autism spectrum disorder [Internet]. Frontiers in Neuroscience. 2017 ;11 1-8.[citado 2024 out. 04 ] Available from: https://doi.org/10.3389/fnins.2017.00016 -
Vancouver
Vidal MC, Sato JR, Balardin JB, Takahashi DY, Fujita A. ANOCVA in R: a software to compare clusters between groups and its application to the study of autism spectrum disorder [Internet]. Frontiers in Neuroscience. 2017 ;11 1-8.[citado 2024 out. 04 ] Available from: https://doi.org/10.3389/fnins.2017.00016 - Identification of segregated regions in the functional brain connectome of autistic patients by a combination of fuzzy spectral clustering and entropy analysis
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Informações sobre o DOI: 10.3389/fnins.2017.00016 (Fonte: oaDOI API)
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