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Artigo de periodico
Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis (2025)
Alves, Caroline Lourenço ; Sallum, Loriz Francisco ; Rodrigues, Francisco Aparecido ; Toutain, Thaise Graziele Lima de ; Aguiar, Patricia Maria de Carvalho ; Moeckel, Michael
Source: Journal of Physics: Complexity. Unidades: ICMC, Interinstitucional de Pós-Graduação em Estatística
Subjects: DOENÇA DE PARKINSON, ELETROENCEFALOGRAFIA, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALVES, Caroline Lourenço et al. Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis. Journal of Physics: Complexity, v. 6, p. 1-18, 2025Tradução . . Disponível em: https://doi.org/10.1088/2632-072X/adf58a. Acesso em: 27 abr. 2026.
APA
Alves, C. L., Sallum, L. F., Rodrigues, F. A., Toutain, T. G. L. de, Aguiar, P. M. de C., & Moeckel, M. (2025). Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis. Journal of Physics: Complexity, 6, 1-18. doi:10.1088/2632-072X/adf58a
NLM
Alves CL, Sallum LF, Rodrigues FA, Toutain TGL de, Aguiar PM de C, Moeckel M. Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis [Internet]. Journal of Physics: Complexity. 2025 ; 6 1-18.[citado 2026 abr. 27 ] Available from: https://doi.org/10.1088/2632-072X/adf58a
Vancouver
Alves CL, Sallum LF, Rodrigues FA, Toutain TGL de, Aguiar PM de C, Moeckel M. Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis [Internet]. Journal of Physics: Complexity. 2025 ; 6 1-18.[citado 2026 abr. 27 ] Available from: https://doi.org/10.1088/2632-072X/adf58a
Artigo de periodico
Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies (2025)
Alves, Caroline Lourenço ; Kuhnert, Katharina; Rodrigues, Francisco Aparecido ; Moeckel, Michael
Source: Biology Methods and Protocols. Unidade: ICMC
Subjects: COVID-19, REDES COMPLEXAS, APRENDIZAGEM PROFUNDA, AVALIAÇÃO DE AÇÕES DE SAÚDE PÚBLICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALVES, Caroline Lourenço et al. Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies. Biology Methods and Protocols, v. 10, n. 1, p. 1-11, 2025Tradução . . Disponível em: https://doi.org/10.1093/biomethods/bpaf039. Acesso em: 27 abr. 2026.
APA
Alves, C. L., Kuhnert, K., Rodrigues, F. A., & Moeckel, M. (2025). Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies. Biology Methods and Protocols, 10( 1), 1-11. doi:10.1093/biomethods/bpaf039
NLM
Alves CL, Kuhnert K, Rodrigues FA, Moeckel M. Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies [Internet]. Biology Methods and Protocols. 2025 ; 10( 1): 1-11.[citado 2026 abr. 27 ] Available from: https://doi.org/10.1093/biomethods/bpaf039
Vancouver
Alves CL, Kuhnert K, Rodrigues FA, Moeckel M. Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies [Internet]. Biology Methods and Protocols. 2025 ; 10( 1): 1-11.[citado 2026 abr. 27 ] Available from: https://doi.org/10.1093/biomethods/bpaf039
Artigo de periodico
Multiclass classification of autism spectrum disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis (2024)
Alves, Caroline Lourenço ; Martinelli, Tiago; Sallum, Loriz Francisco ; Rodrigues, Francisco Aparecido ; Toutain, Thaise Graziele Lima de ; Porto, Joel Augusto Moura ; Thielemann, Christiane ; Aguiar, Patricia Maria de Carvalho ; Moeckel, Michael
Source: PLoS One. Unidades: ICMC, IFSC, Interinstitucional de Pós-Graduação em Estatística
Subjects: APRENDIZADO COMPUTACIONAL, RESSONÂNCIA MAGNÉTICA, BENCHMARKS, TRANSTORNO DO DEFICIT DE ATENÇÃO COM HIPERATIVIDADE, TRANSTORNO DO ESPECTRO AUTISTA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALVES, Caroline Lourenço et al. Multiclass classification of autism spectrum disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis. PLoS One, v. 19, n. 10, p. 1-35, 2024Tradução . . Disponível em: https://doi.org/10.1371/journal.pone.0305630. Acesso em: 27 abr. 2026.
APA
Alves, C. L., Martinelli, T., Sallum, L. F., Rodrigues, F. A., Toutain, T. G. L. de, Porto, J. A. M., et al. (2024). Multiclass classification of autism spectrum disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis. PLoS One, 19( 10), 1-35. doi:10.1371/journal.pone.0305630
NLM
Alves CL, Martinelli T, Sallum LF, Rodrigues FA, Toutain TGL de, Porto JAM, Thielemann C, Aguiar PM de C, Moeckel M. Multiclass classification of autism spectrum disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis [Internet]. PLoS One. 2024 ; 19( 10): 1-35.[citado 2026 abr. 27 ] Available from: https://doi.org/10.1371/journal.pone.0305630
Vancouver
Alves CL, Martinelli T, Sallum LF, Rodrigues FA, Toutain TGL de, Porto JAM, Thielemann C, Aguiar PM de C, Moeckel M. Multiclass classification of autism spectrum disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis [Internet]. PLoS One. 2024 ; 19( 10): 1-35.[citado 2026 abr. 27 ] Available from: https://doi.org/10.1371/journal.pone.0305630

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