Filtros : "Fujita, André" "Journal of Bioinformatics and Computational Biology" Removido: "Elsevier Ltd" Limpar

Filtros



Limitar por data


  • Fonte: Journal of Bioinformatics and Computational Biology. Unidade: IME

    Assuntos: BIOINFORMÁTICA, NEOPLASIAS PULMONARES, MODELOS PARA PROCESSOS ESTOCÁSTICOS

    Versão PublicadaAcesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      RELVAS, Carlos E. M. et al. A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification. Journal of Bioinformatics and Computational Biology, v. 21, n. artigo 2350019, p. 1-26, 2023Tradução . . Disponível em: https://doi.org/10.1142/S0219720023500191. Acesso em: 07 out. 2024.
    • APA

      Relvas, C. E. M., Nakata, A., Chen, G., Beer, D. G., Gotoh, N., & Fujita, A. (2023). A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification. Journal of Bioinformatics and Computational Biology, 21( artigo 2350019), 1-26. doi:10.1142/S0219720023500191
    • NLM

      Relvas CEM, Nakata A, Chen G, Beer DG, Gotoh N, Fujita A. A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification [Internet]. Journal of Bioinformatics and Computational Biology. 2023 ; 21( artigo 2350019): 1-26.[citado 2024 out. 07 ] Available from: https://doi.org/10.1142/S0219720023500191
    • Vancouver

      Relvas CEM, Nakata A, Chen G, Beer DG, Gotoh N, Fujita A. A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification [Internet]. Journal of Bioinformatics and Computational Biology. 2023 ; 21( artigo 2350019): 1-26.[citado 2024 out. 07 ] Available from: https://doi.org/10.1142/S0219720023500191
  • Fonte: Journal of Bioinformatics and Computational Biology. Unidades: IQ, IME

    Assuntos: EXPRESSÃO GÊNICA, BIOQUÍMICA

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      FUJITA, André et al. Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis. Journal of Bioinformatics and Computational Biology, v. 7, n. 4, p. 663-684, 2009Tradução . . Disponível em: https://doi.org/10.1142/S0219720009004230. Acesso em: 07 out. 2024.
    • APA

      Fujita, A., Sato, J. R., Demasi, M. A. A., Sogayar, M. C., Ferreira, C. E., & Miyano, S. (2009). Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis. Journal of Bioinformatics and Computational Biology, 7( 4), 663-684. doi:10.1142/S0219720009004230
    • NLM

      Fujita A, Sato JR, Demasi MAA, Sogayar MC, Ferreira CE, Miyano S. Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis [Internet]. Journal of Bioinformatics and Computational Biology. 2009 ; 7( 4): 663-684.[citado 2024 out. 07 ] Available from: https://doi.org/10.1142/S0219720009004230
    • Vancouver

      Fujita A, Sato JR, Demasi MAA, Sogayar MC, Ferreira CE, Miyano S. Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis [Internet]. Journal of Bioinformatics and Computational Biology. 2009 ; 7( 4): 663-684.[citado 2024 out. 07 ] Available from: https://doi.org/10.1142/S0219720009004230
  • Fonte: Journal of Bioinformatics and Computational Biology. Unidades: EACH, IQ, IME

    Assuntos: EXPRESSÃO GÊNICA, BIOQUÍMICA

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      FUJITA, André et al. Modeling nonlinear gene regulatory networks from time series gene expression data. Journal of Bioinformatics and Computational Biology, v. 6, n. 5, p. 961-979, 2008Tradução . . Disponível em: https://doi.org/10.1142/s0219720008003746. Acesso em: 07 out. 2024.
    • APA

      Fujita, A., Sato, J. R., Garay-Malpartida, H. M., Sogayar, M. C., Ferreira, C. E., & Miyano, S. (2008). Modeling nonlinear gene regulatory networks from time series gene expression data. Journal of Bioinformatics and Computational Biology, 6( 5), 961-979. doi:10.1142/s0219720008003746
    • NLM

      Fujita A, Sato JR, Garay-Malpartida HM, Sogayar MC, Ferreira CE, Miyano S. Modeling nonlinear gene regulatory networks from time series gene expression data [Internet]. Journal of Bioinformatics and Computational Biology. 2008 ; 6( 5): 961-979.[citado 2024 out. 07 ] Available from: https://doi.org/10.1142/s0219720008003746
    • Vancouver

      Fujita A, Sato JR, Garay-Malpartida HM, Sogayar MC, Ferreira CE, Miyano S. Modeling nonlinear gene regulatory networks from time series gene expression data [Internet]. Journal of Bioinformatics and Computational Biology. 2008 ; 6( 5): 961-979.[citado 2024 out. 07 ] Available from: https://doi.org/10.1142/s0219720008003746

Biblioteca Digital de Produção Intelectual da Universidade de São Paulo     2012 - 2024