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Artigo de periodico
Noninvasive prenatal paternity determination using microhaplotypes: a pilot study (2020)
Wang, Jaqueline Yu Ting; Whittle, Martin R. ; Puga, Renato David ; Yambartsev, Anatoli ; Fujita, André ; Nakaya, Helder Takashi Imoto
Source: BMC Medical Genomics. Unidades: IME, FCF, BIOINFORMÁTICA
Subjects: DIAGNÓSTICO PRÉ-NATAL, PATERNIDADE, BIOINFORMÁTICA, HAPLOTIPOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
WANG, Jaqueline Yu Ting et al. Noninvasive prenatal paternity determination using microhaplotypes: a pilot study. BMC Medical Genomics, v. 13, n. art. 157, p. 1-8, 2020Tradução . . Disponível em: https://doi.org/10.1186/s12920-020-00806-w. Acesso em: 19 jun. 2024.
APA
Wang, J. Y. T., Whittle, M. R., Puga, R. D., Yambartsev, A., Fujita, A., & Nakaya, H. T. I. (2020). Noninvasive prenatal paternity determination using microhaplotypes: a pilot study. BMC Medical Genomics, 13( art. 157), 1-8. doi:10.1186/s12920-020-00806-w
NLM
Wang JYT, Whittle MR, Puga RD, Yambartsev A, Fujita A, Nakaya HTI. Noninvasive prenatal paternity determination using microhaplotypes: a pilot study [Internet]. BMC Medical Genomics. 2020 ; 13( art. 157): 1-8.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1186/s12920-020-00806-w
Vancouver
Wang JYT, Whittle MR, Puga RD, Yambartsev A, Fujita A, Nakaya HTI. Noninvasive prenatal paternity determination using microhaplotypes: a pilot study [Internet]. BMC Medical Genomics. 2020 ; 13( art. 157): 1-8.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1186/s12920-020-00806-w
Parte de monografia/livro
ANOCVA: a nonparametric statistical test to compare clustering structures (2018)
Patriota, Alexandre Galvão ; Vidal, Maciel Calebe; Jesus, Davi Augusto Caetano de; Fujita, André
Source: Theoretical and applied aspects of systems biology. Unidade: IME
Subjects: BIOINFORMÁTICA, BIOESTATÍSTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PATRIOTA, Alexandre Galvão et al. ANOCVA: a nonparametric statistical test to compare clustering structures. Theoretical and applied aspects of systems biology. Tradução . Cham: Springer, 2018. . Disponível em: https://doi.org/10.1007/978-3-319-74974-7_6. Acesso em: 19 jun. 2024.
APA
Patriota, A. G., Vidal, M. C., Jesus, D. A. C. de, & Fujita, A. (2018). ANOCVA: a nonparametric statistical test to compare clustering structures. In Theoretical and applied aspects of systems biology. Cham: Springer. doi:10.1007/978-3-319-74974-7_6
NLM
Patriota AG, Vidal MC, Jesus DAC de, Fujita A. ANOCVA: a nonparametric statistical test to compare clustering structures [Internet]. In: Theoretical and applied aspects of systems biology. Cham: Springer; 2018. [citado 2024 jun. 19 ] Available from: https://doi.org/10.1007/978-3-319-74974-7_6
Vancouver
Patriota AG, Vidal MC, Jesus DAC de, Fujita A. ANOCVA: a nonparametric statistical test to compare clustering structures [Internet]. In: Theoretical and applied aspects of systems biology. Cham: Springer; 2018. [citado 2024 jun. 19 ] Available from: https://doi.org/10.1007/978-3-319-74974-7_6
Parte de monografia/livro
Causal inference and structure learning of genotype–phenotype networks using genetic variation (2016)
Ribeiro, Adele Helena; Soler, Júlia Maria Pavan ; Chaibub Neto, Elias; Fujita, André
Source: Big data analytics in genomics. Unidade: IME
Subjects: BIOINFORMÁTICA, VARIAÇÃO GENÉTICA, GENÓTIPOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIBEIRO, Adele Helena et al. Causal inference and structure learning of genotype–phenotype networks using genetic variation. Big data analytics in genomics. Tradução . Cham: Springer, 2016. . Disponível em: https://doi.org/10.1007/978-3-319-41279-5_3. Acesso em: 19 jun. 2024.
APA
Ribeiro, A. H., Soler, J. M. P., Chaibub Neto, E., & Fujita, A. (2016). Causal inference and structure learning of genotype–phenotype networks using genetic variation. In Big data analytics in genomics. Cham: Springer. doi:10.1007/978-3-319-41279-5_3
NLM
Ribeiro AH, Soler JMP, Chaibub Neto E, Fujita A. Causal inference and structure learning of genotype–phenotype networks using genetic variation [Internet]. In: Big data analytics in genomics. Cham: Springer; 2016. [citado 2024 jun. 19 ] Available from: https://doi.org/10.1007/978-3-319-41279-5_3
Vancouver
Ribeiro AH, Soler JMP, Chaibub Neto E, Fujita A. Causal inference and structure learning of genotype–phenotype networks using genetic variation [Internet]. In: Big data analytics in genomics. Cham: Springer; 2016. [citado 2024 jun. 19 ] Available from: https://doi.org/10.1007/978-3-319-41279-5_3
Artigo de periodico
A non-parametric method to estimate the number of clusters (2014)
Fujita, André ; Takahashi, Daniel Yasumasa ; Patriota, Alexandre Galvão
Source: Computational Statistics & Data Analysis. Unidade: IME
Subjects: BIOINFORMÁTICA, ESTATÍSTICA COMPUTACIONAL, ANÁLISE DE CONGLOMERADOS, ANÁLISE ESPECTRAL (ANÁLISE DE SÉRIES TEMPORAIS), HEURÍSTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FUJITA, André e TAKAHASHI, Daniel Yasumasa e PATRIOTA, Alexandre Galvão. A non-parametric method to estimate the number of clusters. Computational Statistics & Data Analysis, v. 73, p. 27-39, 2014Tradução . . Disponível em: https://doi.org/10.1016/j.csda.2013.11.012. Acesso em: 19 jun. 2024.
APA
Fujita, A., Takahashi, D. Y., & Patriota, A. G. (2014). A non-parametric method to estimate the number of clusters. Computational Statistics & Data Analysis, 73, 27-39. doi:10.1016/j.csda.2013.11.012
NLM
Fujita A, Takahashi DY, Patriota AG. A non-parametric method to estimate the number of clusters [Internet]. Computational Statistics & Data Analysis. 2014 ; 73 27-39.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1016/j.csda.2013.11.012
Vancouver
Fujita A, Takahashi DY, Patriota AG. A non-parametric method to estimate the number of clusters [Internet]. Computational Statistics & Data Analysis. 2014 ; 73 27-39.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1016/j.csda.2013.11.012
Artigo de periodico
Functional clustering of time series gene expression data by Granger causality (2012)
Fujita, André ; Severino, Patrícia ; Kaname, Kojima ; Sato, João Ricardo ; Patriota, Alexandre Galvão ; Miyano, Satoru
Source: BMC Systems Biology. Unidade: IME
Subjects: BIOQUÍMICA, ANÁLISE DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FUJITA, André et al. Functional clustering of time series gene expression data by Granger causality. BMC Systems Biology, v. 6, n. 137, p. 1-12, 2012Tradução . . Disponível em: https://doi.org/10.1186/1752-0509-6-137. Acesso em: 19 jun. 2024.
APA
Fujita, A., Severino, P., Kaname, K., Sato, J. R., Patriota, A. G., & Miyano, S. (2012). Functional clustering of time series gene expression data by Granger causality. BMC Systems Biology, 6( 137), 1-12. doi:10.1186/1752-0509-6-137
NLM
Fujita A, Severino P, Kaname K, Sato JR, Patriota AG, Miyano S. Functional clustering of time series gene expression data by Granger causality [Internet]. BMC Systems Biology. 2012 ; 6( 137): 1-12.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1186/1752-0509-6-137
Vancouver
Fujita A, Severino P, Kaname K, Sato JR, Patriota AG, Miyano S. Functional clustering of time series gene expression data by Granger causality [Internet]. BMC Systems Biology. 2012 ; 6( 137): 1-12.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1186/1752-0509-6-137
Artigo de periodico
DWT-CEM: an algorithm for scale-temporal clustering in fMRI (2007)
Sato, João Ricardo; Fujita, André ; Amaro Junior, Edson; Miranda, Janaina Mourão; Morettin, Pedro Alberto ; Brammer, Michal John
Source: Biological cybernetics. Unidades: FM, IME, BIOINFORMÁTICA
Subjects: ESTATÍSTICA (TENDÊNCIAS), ALGORITMOS E ESTRUTURAS DE DADOS, IMAGEM POR RESSONÂNCIA MAGNÉTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SATO, João Ricardo et al. DWT-CEM: an algorithm for scale-temporal clustering in fMRI. Biological cybernetics, v. 97, n. 1, p. 33-45, 2007Tradução . . Disponível em: https://doi.org/10.1007/s00422-007-0154-4. Acesso em: 19 jun. 2024.
APA
Sato, J. R., Fujita, A., Amaro Junior, E., Miranda, J. M., Morettin, P. A., & Brammer, M. J. (2007). DWT-CEM: an algorithm for scale-temporal clustering in fMRI. Biological cybernetics, 97( 1), 33-45. doi:10.1007/s00422-007-0154-4
NLM
Sato JR, Fujita A, Amaro Junior E, Miranda JM, Morettin PA, Brammer MJ. DWT-CEM: an algorithm for scale-temporal clustering in fMRI [Internet]. Biological cybernetics. 2007 ; 97( 1): 33-45.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1007/s00422-007-0154-4
Vancouver
Sato JR, Fujita A, Amaro Junior E, Miranda JM, Morettin PA, Brammer MJ. DWT-CEM: an algorithm for scale-temporal clustering in fMRI [Internet]. Biological cybernetics. 2007 ; 97( 1): 33-45.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1007/s00422-007-0154-4
Artigo de periodico
Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method (2007)
Fujita, André ; Sato, João Ricardo ; Garay-Malpartida, Humberto Miguel ; Morettin, Pedro Alberto ; Sogayar, Mari Cleide ; Ferreira, Carlos Eduardo
Source: Bioinformatics. Unidades: IME, IQ, BIOINFORMÁTICA
Subjects: EXPRESSÃO GÊNICA, BIOQUÍMICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FUJITA, André et al. Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method. Bioinformatics, v. 23, n. 13, p. 1623-1630, 2007Tradução . . Disponível em: https://doi.org/10.1093/bioinformatics/btm151. Acesso em: 19 jun. 2024.
APA
Fujita, A., Sato, J. R., Garay-Malpartida, H. M., Morettin, P. A., Sogayar, M. C., & Ferreira, C. E. (2007). Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method. Bioinformatics, 23( 13), 1623-1630. doi:10.1093/bioinformatics/btm151
NLM
Fujita A, Sato JR, Garay-Malpartida HM, Morettin PA, Sogayar MC, Ferreira CE. Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method [Internet]. Bioinformatics. 2007 ; 23( 13): 1623-1630.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1093/bioinformatics/btm151
Vancouver
Fujita A, Sato JR, Garay-Malpartida HM, Morettin PA, Sogayar MC, Ferreira CE. Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method [Internet]. Bioinformatics. 2007 ; 23( 13): 1623-1630.[citado 2024 jun. 19 ] Available from: https://doi.org/10.1093/bioinformatics/btm151

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