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Artigo de periodico
A mixture model for determining SARS-Cov-2 variant composition in pooled samples (2022)
Valieris, Renan; Drummond, Rodrigo D.; Defelicibus, Alexandre; Dias Neto, Emmanuel; Mitrowsky, Rafael Andres Rosales ; Silva, Israel Tojal da
Source: Bioinformatics. Unidades: BIOINFORMÁTICA, ICMC, FFCLRP
Subjects: VACINAS, COVID-19, PANDEMIAS, TRANSMISSÃO DE DOENÇAS, MODELOS MATEMÁTICOS, PROGRAMAS DE VACINAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VALIERIS, Renan et al. A mixture model for determining SARS-Cov-2 variant composition in pooled samples. Bioinformatics, v. 38, n. 7, p. 1809-1815, 2022Tradução . . Disponível em: https://doi.org/10.1093/bioinformatics/btac047. Acesso em: 05 nov. 2024.
APA
Valieris, R., Drummond, R. D., Defelicibus, A., Dias Neto, E., Mitrowsky, R. A. R., & Silva, I. T. da. (2022). A mixture model for determining SARS-Cov-2 variant composition in pooled samples. Bioinformatics, 38( 7), 1809-1815. doi:10.1093/bioinformatics/btac047
NLM
Valieris R, Drummond RD, Defelicibus A, Dias Neto E, Mitrowsky RAR, Silva IT da. A mixture model for determining SARS-Cov-2 variant composition in pooled samples [Internet]. Bioinformatics. 2022 ; 38( 7): 1809-1815.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1093/bioinformatics/btac047
Vancouver
Valieris R, Drummond RD, Defelicibus A, Dias Neto E, Mitrowsky RAR, Silva IT da. A mixture model for determining SARS-Cov-2 variant composition in pooled samples [Internet]. Bioinformatics. 2022 ; 38( 7): 1809-1815.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1093/bioinformatics/btac047
Trabalho de evento-resumo periodico
Microbiome studies of the built environment: from commensals, to cancer & COVID-19 (2021)
Simão, Andréa Name Colado; Silva, Israel Tojal da ; Silva Junior, Wilson Araújo da ; Dias-Neto, Emmanuel
Source: Biophysical Reviews. Conference titles: IUPAB International Congress. Unidades: FMRP, ICMC, BIOINFORMÁTICA
Subjects: HELICOBACTER PYLORI, MICROBIOLOGIA, CORONAVIRUS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SIMÃO, Andréa Name Colado et al. Microbiome studies of the built environment: from commensals, to cancer & COVID-19. Biophysical Reviews. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007%2Fs12551-021-00845-2. Acesso em: 05 nov. 2024. , 2021
APA
Simão, A. N. C., Silva, I. T. da, Silva Junior, W. A. da, & Dias-Neto, E. (2021). Microbiome studies of the built environment: from commensals, to cancer & COVID-19. Biophysical Reviews. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007%2Fs12551-021-00845-2
NLM
Simão ANC, Silva IT da, Silva Junior WA da, Dias-Neto E. Microbiome studies of the built environment: from commensals, to cancer & COVID-19 [Internet]. Biophysical Reviews. 2021 ; 13 1283-1472.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007%2Fs12551-021-00845-2
Vancouver
Simão ANC, Silva IT da, Silva Junior WA da, Dias-Neto E. Microbiome studies of the built environment: from commensals, to cancer & COVID-19 [Internet]. Biophysical Reviews. 2021 ; 13 1283-1472.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007%2Fs12551-021-00845-2
Trabalho de evento-anais periodico
Deep convolutional neural networks for plant species characterization based on leaf midrib (2019)
Scabini, Leonardo Felipe dos Santos; Condori, Rayner Harold Montes ; Munhoz, Isabella de Cassia Lima; Bruno, Odemir Martinez
Source: Lecture Notes in Computer Science - LNCS. Conference titles: International Conference on Computer Analysis of Images and Patterns - CAIP. Unidades: IFSC, ICMC, BIOINFORMÁTICA
Subjects: RECONHECIMENTO DE PADRÕES, REDES NEURAIS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SCABINI, Leonardo Felipe dos Santos et al. Deep convolutional neural networks for plant species characterization based on leaf midrib. Lecture Notes in Computer Science - LNCS. Heidelberg: Springer. Disponível em: https://doi.org/10.1007/978-3-030-29891-3_34. Acesso em: 05 nov. 2024. , 2019
APA
Scabini, L. F. dos S., Condori, R. H. M., Munhoz, I. de C. L., & Bruno, O. M. (2019). Deep convolutional neural networks for plant species characterization based on leaf midrib. Lecture Notes in Computer Science - LNCS. Heidelberg: Springer. doi:10.1007/978-3-030-29891-3_34
NLM
Scabini LF dos S, Condori RHM, Munhoz I de CL, Bruno OM. Deep convolutional neural networks for plant species characterization based on leaf midrib [Internet]. Lecture Notes in Computer Science - LNCS. 2019 ; 11679 389-401.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-29891-3_34
Vancouver
Scabini LF dos S, Condori RHM, Munhoz I de CL, Bruno OM. Deep convolutional neural networks for plant species characterization based on leaf midrib [Internet]. Lecture Notes in Computer Science - LNCS. 2019 ; 11679 389-401.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-29891-3_34

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