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Artigo de periodico
A machine learning approach to predicting dynamical observables from network structure (2024)
Rodrigues, Francisco Aparecido ; Peron, Thomas ; Connaughton, Colm ; Kurths, Jürgen ; Moreno, Yamir
Source: Proceedings of the Royal Society A. Unidade: ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, SISTEMAS DINÂMICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RODRIGUES, Francisco Aparecido et al. A machine learning approach to predicting dynamical observables from network structure. Proceedings of the Royal Society A, v. 481, p. 1-12, 2024Tradução . . Disponível em: https://doi.org/10.1098/rspa.2024.0435. Acesso em: 08 out. 2025.
APA
Rodrigues, F. A., Peron, T., Connaughton, C., Kurths, J., & Moreno, Y. (2024). A machine learning approach to predicting dynamical observables from network structure. Proceedings of the Royal Society A, 481, 1-12. doi:10.1098/rspa.2024.0435
NLM
Rodrigues FA, Peron T, Connaughton C, Kurths J, Moreno Y. A machine learning approach to predicting dynamical observables from network structure [Internet]. Proceedings of the Royal Society A. 2024 ; 481 1-12.[citado 2025 out. 08 ] Available from: https://doi.org/10.1098/rspa.2024.0435
Vancouver
Rodrigues FA, Peron T, Connaughton C, Kurths J, Moreno Y. A machine learning approach to predicting dynamical observables from network structure [Internet]. Proceedings of the Royal Society A. 2024 ; 481 1-12.[citado 2025 out. 08 ] Available from: https://doi.org/10.1098/rspa.2024.0435
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
Source: Proceedings. Conference titles: IEEE International Conference on Semantic Computing - ICSC. Unidades: ICMC, IFSC, FCFRP
Subjects: 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 out. 2025.
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 2025 out. 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 2025 out. 08 ] Available from: https://doi.org/10.1109/ICSC56153.2023.00039
Artigo de periodico
Development of a novel chemoinformatic tool for natural product databases (2023)
Carmo, Paulo Ricardo Viviurka do ; Marcacini, Ricardo Marcondes ; Valli, Marilia ; Silva-Silva, João Victor ; Ferreira, Leonardo Luiz Gomes ; Pilon, Alan Cesar ; Bolzani, Vanderlan da Silva ; Andricopulo, Adriano Defini ; Marx, Edgard
Source: Future Drug Discovery. Unidades: ICMC, IFSC
Subjects: DESCOBERTA DE CONHECIMENTO, APRENDIZADO COMPUTACIONAL, BANCO DE DADOS, FÁRMACOS, PRODUTOS NATURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARMO, Paulo Ricardo Viviurka do et al. Development of a novel chemoinformatic tool for natural product databases. Future Drug Discovery, v. 5, n. 2, p. 1-11, 2023Tradução . . Disponível em: https://doi.org/10.4155/fdd-2023-0007. Acesso em: 08 out. 2025.
APA
Carmo, P. R. V. do, Marcacini, R. M., Valli, M., Silva-Silva, J. V., Ferreira, L. L. G., Pilon, A. C., et al. (2023). Development of a novel chemoinformatic tool for natural product databases. Future Drug Discovery, 5( 2), 1-11. doi:10.4155/fdd-2023-0007
NLM
Carmo PRV do, Marcacini RM, Valli M, Silva-Silva JV, Ferreira LLG, Pilon AC, Bolzani V da S, Andricopulo AD, Marx E. Development of a novel chemoinformatic tool for natural product databases [Internet]. Future Drug Discovery. 2023 ; 5( 2): 1-11.[citado 2025 out. 08 ] Available from: https://doi.org/10.4155/fdd-2023-0007
Vancouver
Carmo PRV do, Marcacini RM, Valli M, Silva-Silva JV, Ferreira LLG, Pilon AC, Bolzani V da S, Andricopulo AD, Marx E. Development of a novel chemoinformatic tool for natural product databases [Internet]. Future Drug Discovery. 2023 ; 5( 2): 1-11.[citado 2025 out. 08 ] Available from: https://doi.org/10.4155/fdd-2023-0007
Artigo de periodico-carta/editorial
The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] (2022)
Soares, Thereza A. ; Alves, Ariane Ferreira Nunes; Mazzolari, Angelica; Ruggiu, Fiorella; Wei, Guo-Wei; Merz, Kenneth
Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS MATEMÁTICOS, ESTRUTURA MOLECULAR (QUÍMICA TEÓRICA)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOARES, Thereza A. et al. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1021/acs.jcim.2c01422. Acesso em: 08 out. 2025. , 2022
APA
Soares, T. A., Alves, A. F. N., Mazzolari, A., Ruggiu, F., Wei, G. -W., & Merz, K. (2022). The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1021/acs.jcim.2c01422
NLM
Soares TA, Alves AFN, Mazzolari A, Ruggiu F, Wei G-W, Merz K. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 22): 5317-5320.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422
Vancouver
Soares TA, Alves AFN, Mazzolari A, Ruggiu F, Wei G-W, Merz K. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 22): 5317-5320.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422
Artigo de periodico
CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems (2021)
Alkhnbashi, Omer S ; Mitrofanov, Alexander; Bonidia, Robson Parmezan ; Raden, Martin ; Tran, Van Dinh ; Eggenhofer, Florian ; Shah, Shiraz A ; Ozturk, Ekrem; Padilha, Victor Alexandre ; Sanches, Danilo Sipoli ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Backofen, Rolf
Source: Nucleic Acids Research. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, GENES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALKHNBASHI, Omer S et al. CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems. Nucleic Acids Research, v. 49, p. w125-w130, 2021Tradução . . Disponível em: https://doi.org/10.1093/nar/gkab456. Acesso em: 08 out. 2025.
APA
Alkhnbashi, O. S., Mitrofanov, A., Bonidia, R. P., Raden, M., Tran, V. D., Eggenhofer, F., et al. (2021). CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems. Nucleic Acids Research, 49, w125-w130. doi:10.1093/nar/gkab456
NLM
Alkhnbashi OS, Mitrofanov A, Bonidia RP, Raden M, Tran VD, Eggenhofer F, Shah SA, Ozturk E, Padilha VA, Sanches DS, Carvalho ACP de LF de, Backofen R. CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems [Internet]. Nucleic Acids Research. 2021 ; 49 w125-w130.[citado 2025 out. 08 ] Available from: https://doi.org/10.1093/nar/gkab456
Vancouver
Alkhnbashi OS, Mitrofanov A, Bonidia RP, Raden M, Tran VD, Eggenhofer F, Shah SA, Ozturk E, Padilha VA, Sanches DS, Carvalho ACP de LF de, Backofen R. CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems [Internet]. Nucleic Acids Research. 2021 ; 49 w125-w130.[citado 2025 out. 08 ] Available from: https://doi.org/10.1093/nar/gkab456
Artigo de periodico
Casboundary: automated definition of integral Cas cassettes (2021)
Padilha, Victor Alexandre ; Alkhnbashi, Omer S ; Tran, Van Dinh ; Shah, Shiraz A ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Backofen, Rolf
Source: Bioinformatics. Unidade: ICMC
Subjects: BIOINFORMÁTICA, APRENDIZADO COMPUTACIONAL, GENES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PADILHA, Victor Alexandre et al. Casboundary: automated definition of integral Cas cassettes. Bioinformatics, v. 37, n. 10, p. 1352-1359, 2021Tradução . . Disponível em: https://doi.org/10.1093/bioinformatics/btaa984. Acesso em: 08 out. 2025.
APA
Padilha, V. A., Alkhnbashi, O. S., Tran, V. D., Shah, S. A., Carvalho, A. C. P. de L. F. de, & Backofen, R. (2021). Casboundary: automated definition of integral Cas cassettes. Bioinformatics, 37( 10), 1352-1359. doi:10.1093/bioinformatics/btaa984
NLM
Padilha VA, Alkhnbashi OS, Tran VD, Shah SA, Carvalho ACP de LF de, Backofen R. Casboundary: automated definition of integral Cas cassettes [Internet]. Bioinformatics. 2021 ; 37( 10): 1352-1359.[citado 2025 out. 08 ] Available from: https://doi.org/10.1093/bioinformatics/btaa984
Vancouver
Padilha VA, Alkhnbashi OS, Tran VD, Shah SA, Carvalho ACP de LF de, Backofen R. Casboundary: automated definition of integral Cas cassettes [Internet]. Bioinformatics. 2021 ; 37( 10): 1352-1359.[citado 2025 out. 08 ] Available from: https://doi.org/10.1093/bioinformatics/btaa984

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