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Artigo de periodico
Machine learning models for the identification of natural products with anti-schistosomiasis activity (2025)
Angelo, Rafaela Molina de ; Maltarollo, Vinícius Gonçalves ; Lago, João Henrique Ghilardi ; Honorio, Kathia Maria
Source: Journal Brazilian Chemical Society. Unidade: EACH
Assunto: ESQUISTOSSOMOSE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ANGELO, Rafaela Molina de et al. Machine learning models for the identification of natural products with anti-schistosomiasis activity. Journal Brazilian Chemical Society, v. 36, n. 8, p. 01-18, 2025Tradução . . Disponível em: http://dx.doi.org/10.21577/0103-5053.20250070. Acesso em: 13 nov. 2025.
APA
Angelo, R. M. de, Maltarollo, V. G., Lago, J. H. G., & Honorio, K. M. (2025). Machine learning models for the identification of natural products with anti-schistosomiasis activity. Journal Brazilian Chemical Society, 36( 8), 01-18. doi:10.21577/0103-5053.20250070
NLM
Angelo RM de, Maltarollo VG, Lago JHG, Honorio KM. Machine learning models for the identification of natural products with anti-schistosomiasis activity [Internet]. Journal Brazilian Chemical Society. 2025 ; 36( 8): 01-18.[citado 2025 nov. 13 ] Available from: http://dx.doi.org/10.21577/0103-5053.20250070
Vancouver
Angelo RM de, Maltarollo VG, Lago JHG, Honorio KM. Machine learning models for the identification of natural products with anti-schistosomiasis activity [Internet]. Journal Brazilian Chemical Society. 2025 ; 36( 8): 01-18.[citado 2025 nov. 13 ] Available from: http://dx.doi.org/10.21577/0103-5053.20250070
Artigo de periodico
MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling (2023)
Veríssimo, Gabriel Corrêa ; Simone Queiroz Panteleão; Gertrudes, Jadson Castro ; Kronenberger, Thales ; Honorio, Kathia Maria ; Maltarollo, Vinícius Gonçalves
Source: Journal of Computer - Aided Molecular Design. Unidade: EACH
Assunto: AMOSTRAGEM
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ABNT
VERÍSSIMO, Gabriel Corrêa et al. MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling. Journal of Computer - Aided Molecular Design, p. 01-26, 2023Tradução . . Disponível em: http://dx.doi.org/10.1007/s10822-023-00536-y. Acesso em: 13 nov. 2025.
APA
Veríssimo, G. C., Simone Queiroz Panteleão,, Gertrudes, J. C., Kronenberger, T., Honorio, K. M., & Maltarollo, V. G. (2023). MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling. Journal of Computer - Aided Molecular Design, 01-26. doi:10.1007/s10822-023-00536-y
NLM
Veríssimo GC, Simone Queiroz Panteleão, Gertrudes JC, Kronenberger T, Honorio KM, Maltarollo VG. MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling [Internet]. Journal of Computer - Aided Molecular Design. 2023 ; 01-26.[citado 2025 nov. 13 ] Available from: http://dx.doi.org/10.1007/s10822-023-00536-y
Vancouver
Veríssimo GC, Simone Queiroz Panteleão, Gertrudes JC, Kronenberger T, Honorio KM, Maltarollo VG. MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling [Internet]. Journal of Computer - Aided Molecular Design. 2023 ; 01-26.[citado 2025 nov. 13 ] Available from: http://dx.doi.org/10.1007/s10822-023-00536-y
Artigo de periodico
Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology (2022)
Bernardes, Rodrigo Cupertino ; De Medeiros, André ; Silva, Laercio ; Cantoni, Leo; Martins, Gustavo Ferreira ; Mastrangelo, Thiago de Araújo ; Novikov, Arthur ; Mastrangelo, Clíssia Barboza
Source: Agriculture. Unidade: CENA
Subjects: SEMENTES, FUSARIUM, IMAGEM DIGITAL, DOENÇAS DE PLANTAS
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ABNT
BERNARDES, Rodrigo Cupertino et al. Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology. Agriculture, v. 12, 2022Tradução . . Disponível em: https://doi.org/10.3390/agriculture12111801. Acesso em: 13 nov. 2025.
APA
Bernardes, R. C., De Medeiros, A., Silva, L., Cantoni, L., Martins, G. F., Mastrangelo, T. de A., et al. (2022). Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology. Agriculture, 12. doi:10.3390/agriculture12111801
NLM
Bernardes RC, De Medeiros A, Silva L, Cantoni L, Martins GF, Mastrangelo T de A, Novikov A, Mastrangelo CB. Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology [Internet]. Agriculture. 2022 ; 12[citado 2025 nov. 13 ] Available from: https://doi.org/10.3390/agriculture12111801
Vancouver
Bernardes RC, De Medeiros A, Silva L, Cantoni L, Martins GF, Mastrangelo T de A, Novikov A, Mastrangelo CB. Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology [Internet]. Agriculture. 2022 ; 12[citado 2025 nov. 13 ] Available from: https://doi.org/10.3390/agriculture12111801
Trabalho de evento
Geodesign Brazil: trees for the metropolitan area of São Paulo (2021)
Sandre, Adriana Afonso ; Fruehauf, Amanda Lombardo ; Miyahara, Augusto Akio Lucchezi ; Rosa, Ashiley Adelaide; Maruyama, Cintia Miua ; Locosselli, Giuliano Maselli ; Candido, Leticia Figueiredo; Lombardo, Magda Adelaide ; Coelho, Matheus Aguiar; Murolo, Rafael Pollastrini ; Pombo, Riciane Maria Reis; Marques, Taícia Helena Negrin ; Pellegrino, Paulo Renato Mesquita
Source: Computational science and its applications. Lecture Notes in Computer Science, vol 12954. Conference titles: Computational Science and Its Applications – ICCSA. Unidades: FAU, ESALQ
Subjects: ARBORIZAÇÃO, ÁREAS METROPOLITANAS, ÁRVORES, DESIGN, FRAMEWORKS, GEOPROCESSAMENTO, SEQUESTRO DE CARBONO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANDRE, Adriana Afonso et al. Geodesign Brazil: trees for the metropolitan area of São Paulo. 2021, Anais.. Cham: Faculdade de Arquitetura e Urbanismo, Universidade de São Paulo, 2021. Disponível em: https://doi.org/10.1007/978-3-030-86979-3_33. Acesso em: 13 nov. 2025.
APA
Sandre, A. A., Fruehauf, A. L., Miyahara, A. A. L., Rosa, A. A., Maruyama, C. M., Locosselli, G. M., et al. (2021). Geodesign Brazil: trees for the metropolitan area of São Paulo. In Computational science and its applications. Lecture Notes in Computer Science, vol 12954. Cham: Faculdade de Arquitetura e Urbanismo, Universidade de São Paulo. doi:10.1007/978-3-030-86979-3_33
NLM
Sandre AA, Fruehauf AL, Miyahara AAL, Rosa AA, Maruyama CM, Locosselli GM, Candido LF, Lombardo MA, Coelho MA, Murolo RP, Pombo RMR, Marques THN, Pellegrino PRM. Geodesign Brazil: trees for the metropolitan area of São Paulo [Internet]. Computational science and its applications. Lecture Notes in Computer Science, vol 12954. 2021 ;[citado 2025 nov. 13 ] Available from: https://doi.org/10.1007/978-3-030-86979-3_33
Vancouver
Sandre AA, Fruehauf AL, Miyahara AAL, Rosa AA, Maruyama CM, Locosselli GM, Candido LF, Lombardo MA, Coelho MA, Murolo RP, Pombo RMR, Marques THN, Pellegrino PRM. Geodesign Brazil: trees for the metropolitan area of São Paulo [Internet]. Computational science and its applications. Lecture Notes in Computer Science, vol 12954. 2021 ;[citado 2025 nov. 13 ] Available from: https://doi.org/10.1007/978-3-030-86979-3_33

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