ReP USP - Resultado da busca

Trabalho de evento
NatUKE: a benchmark for natural product knowledge extraction from academic literature (2023)
Carmo, Paulo Viviurka do ; Marx, Edgard ; Marcacini, Ricardo Marcondes ; Valli, Marilia ; Silva-Silva, João Victor ; Pilon, Alan
Fonte: Proceedings. Nome do evento: IEEE International Conference on Semantic Computing - ICSC. Unidades: ICMC, IFSC, FCFRP
Assuntos: DESCOBERTA DE CONHECIMENTO, MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL, BENCHMARKS, PRODUTOS NATURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARMO, Paulo Viviurka do et al. NatUKE: a benchmark for natural product knowledge extraction from academic literature. 2023, Anais.. Los Alamitos: IEEE, 2023. Disponível em: https://doi.org/10.1109/ICSC56153.2023.00039. Acesso em: 08 ago. 2024.
APA
Carmo, P. V. do, Marx, E., Marcacini, R. M., Valli, M., Silva-Silva, J. V., & Pilon, A. (2023). NatUKE: a benchmark for natural product knowledge extraction from academic literature. In Proceedings. Los Alamitos: IEEE. doi:10.1109/ICSC56153.2023.00039
NLM
Carmo PV do, Marx E, Marcacini RM, Valli M, Silva-Silva JV, Pilon A. NatUKE: a benchmark for natural product knowledge extraction from academic literature [Internet]. Proceedings. 2023 ;[citado 2024 ago. 08 ] Available from: https://doi.org/10.1109/ICSC56153.2023.00039
Vancouver
Carmo PV do, Marx E, Marcacini RM, Valli M, Silva-Silva JV, Pilon A. NatUKE: a benchmark for natural product knowledge extraction from academic literature [Internet]. Proceedings. 2023 ;[citado 2024 ago. 08 ] Available from: https://doi.org/10.1109/ICSC56153.2023.00039
Trabalho de evento-resumo periodico
Novel bioinformatics approaches for Rna analysis may reveal common promising molecular markers between three pediatric tumors (2021)
Silva, Andréa Martins da; Popolin Neto, Mário ; Paulovich, Fernando Vieira ; Salomão, Karina Bezerra; Silva, Keteryne Rodrigues da ; Veronez, Luciana Chain ; Silva Junior, Wilson Araújo da ; Valera, Elvis Terci; Scrideli, Carlos Alberto ; Tone, Luiz Gonzaga
Fonte: Pediatric Blood and Cancer. Nome do evento: Congress of the International Society of Paediatric Oncology - SIOP. Unidades: ICMC, FMRP, FCFRP
Assuntos: BIOINFORMÁTICA, APRENDIZADO COMPUTACIONAL, VISUALIZAÇÃO, BIOMARCADORES, NEOPLASIAS, MEDULOBLASTOMA, CRIANÇAS
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ABNT
SILVA, Andréa Martins da et al. Novel bioinformatics approaches for Rna analysis may reveal common promising molecular markers between three pediatric tumors. Pediatric Blood and Cancer. Hoboken: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Disponível em: https://doi.org/10.1002/pbc.29349. Acesso em: 08 ago. 2024. , 2021
APA
Silva, A. M. da, Popolin Neto, M., Paulovich, F. V., Salomão, K. B., Silva, K. R. da, Veronez, L. C., et al. (2021). Novel bioinformatics approaches for Rna analysis may reveal common promising molecular markers between three pediatric tumors. Pediatric Blood and Cancer. Hoboken: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. doi:10.1002/pbc.29349
NLM
Silva AM da, Popolin Neto M, Paulovich FV, Salomão KB, Silva KR da, Veronez LC, Silva Junior WA da, Valera ET, Scrideli CA, Tone LG. Novel bioinformatics approaches for Rna analysis may reveal common promising molecular markers between three pediatric tumors [Internet]. Pediatric Blood and Cancer. 2021 ; No 2021( S5): S541-542.[citado 2024 ago. 08 ] Available from: https://doi.org/10.1002/pbc.29349
Vancouver
Silva AM da, Popolin Neto M, Paulovich FV, Salomão KB, Silva KR da, Veronez LC, Silva Junior WA da, Valera ET, Scrideli CA, Tone LG. Novel bioinformatics approaches for Rna analysis may reveal common promising molecular markers between three pediatric tumors [Internet]. Pediatric Blood and Cancer. 2021 ; No 2021( S5): S541-542.[citado 2024 ago. 08 ] Available from: https://doi.org/10.1002/pbc.29349
Artigo de periodico
Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques (2020)
Costa, Nattane Luíza da; Ximenez, João Paulo Bianchi; Rodrigues, Jairo Lisboa; Barbosa Junior, Fernando ; Barbosa, Rommel
Fonte: European Food Research and Technology. Unidade: FCFRP
Assuntos: VINHO, APRENDIZADO COMPUTACIONAL, ESPECTROMETRIA DE MASSAS
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ABNT
COSTA, Nattane Luíza da et al. Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques. European Food Research and Technology, v. 246, n. 6, p. 1193-1205, 2020Tradução . . Disponível em: https://doi.org/10.1007/s00217-020-03480-5. Acesso em: 08 ago. 2024.
APA
Costa, N. L. da, Ximenez, J. P. B., Rodrigues, J. L., Barbosa Junior, F., & Barbosa, R. (2020). Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques. European Food Research and Technology, 246( 6), 1193-1205. doi:10.1007/s00217-020-03480-5
NLM
Costa NL da, Ximenez JPB, Rodrigues JL, Barbosa Junior F, Barbosa R. Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques [Internet]. European Food Research and Technology. 2020 ; 246( 6): 1193-1205.[citado 2024 ago. 08 ] Available from: https://doi.org/10.1007/s00217-020-03480-5
Vancouver
Costa NL da, Ximenez JPB, Rodrigues JL, Barbosa Junior F, Barbosa R. Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques [Internet]. European Food Research and Technology. 2020 ; 246( 6): 1193-1205.[citado 2024 ago. 08 ] Available from: https://doi.org/10.1007/s00217-020-03480-5
Artigo de periodico
Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review (2019)
Maione, Camila; Barbosa Junior, Fernando ; Barbosa, Rommel Melgaço
Fonte: Computers and Electronics in Agriculture. Unidade: FCFRP
Assuntos: ANÁLISE DE DADOS, APRENDIZADO COMPUTACIONAL, ADULTERAÇÃO DE ALIMENTOS, MEL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAIONE, Camila e BARBOSA JUNIOR, Fernando e BARBOSA, Rommel Melgaço. Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review. Computers and Electronics in Agriculture, v. 157, p. 436-446, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2019.01.020. Acesso em: 08 ago. 2024.
APA
Maione, C., Barbosa Junior, F., & Barbosa, R. M. (2019). Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review. Computers and Electronics in Agriculture, 157, 436-446. doi:10.1016/j.compag.2019.01.020
NLM
Maione C, Barbosa Junior F, Barbosa RM. Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review [Internet]. Computers and Electronics in Agriculture. 2019 ; 157 436-446.[citado 2024 ago. 08 ] Available from: https://doi.org/10.1016/j.compag.2019.01.020
Vancouver
Maione C, Barbosa Junior F, Barbosa RM. Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review [Internet]. Computers and Electronics in Agriculture. 2019 ; 157 436-446.[citado 2024 ago. 08 ] Available from: https://doi.org/10.1016/j.compag.2019.01.020

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