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Artigo de periodico
Learning by sampling: learning behavioral family models from software product lines (2021)
Damasceno, Carlos Diego Nascimento ; Mousavi, Mohammad Reza ; Simão, Adenilso da Silva
Source: Empirical Software Engineering. Unidade: ICMC
Subjects: MODELOS DE PROCESSO DE SOFTWARE, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DAMASCENO, Carlos Diego Nascimento e MOUSAVI, Mohammad Reza e SIMÃO, Adenilso da Silva. Learning by sampling: learning behavioral family models from software product lines. Empirical Software Engineering, v. 26, n. Ja 2021, p. 1-46, 2021Tradução . . Disponível em: https://doi.org/10.1007/s10664-020-09912-w. Acesso em: 05 out. 2024.
APA
Damasceno, C. D. N., Mousavi, M. R., & Simão, A. da S. (2021). Learning by sampling: learning behavioral family models from software product lines. Empirical Software Engineering, 26( Ja 2021), 1-46. doi:10.1007/s10664-020-09912-w
NLM
Damasceno CDN, Mousavi MR, Simão A da S. Learning by sampling: learning behavioral family models from software product lines [Internet]. Empirical Software Engineering. 2021 ; 26( Ja 2021): 1-46.[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/s10664-020-09912-w
Vancouver
Damasceno CDN, Mousavi MR, Simão A da S. Learning by sampling: learning behavioral family models from software product lines [Internet]. Empirical Software Engineering. 2021 ; 26( Ja 2021): 1-46.[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/s10664-020-09912-w
Artigo de periodico
Model-based test case generation from UML sequence diagrams using extended finite state machines (2021)
Rocha, Mauricio Rego Mota da ; Simão, Adenilso da Silva ; Sousa, Thiago
Source: Software Quality Journal. Unidade: ICMC
Subjects: AUTOMAÇÃO INDUSTRIAL, TESTE E AVALIAÇÃO DE SOFTWARE, ESTUDO DE CASO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ROCHA, Mauricio Rego Mota da e SIMÃO, Adenilso da Silva e SOUSA, Thiago. Model-based test case generation from UML sequence diagrams using extended finite state machines. Software Quality Journal, v. 29, p. 597-627, 2021Tradução . . Disponível em: https://doi.org/10.1007/s11219-020-09531-0. Acesso em: 05 out. 2024.
APA
Rocha, M. R. M. da, Simão, A. da S., & Sousa, T. (2021). Model-based test case generation from UML sequence diagrams using extended finite state machines. Software Quality Journal, 29, 597-627. doi:10.1007/s11219-020-09531-0
NLM
Rocha MRM da, Simão A da S, Sousa T. Model-based test case generation from UML sequence diagrams using extended finite state machines [Internet]. Software Quality Journal. 2021 ; 29 597-627.[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/s11219-020-09531-0
Vancouver
Rocha MRM da, Simão A da S, Sousa T. Model-based test case generation from UML sequence diagrams using extended finite state machines [Internet]. Software Quality Journal. 2021 ; 29 597-627.[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/s11219-020-09531-0
Tese (Doutorado)
Computational approaches for the discovery of significant genes in cancer (2021)
Cutigi, Jorge Francisco; Evangelista, Adriane Feijó (Orientador); Simão, Adenilso da Silva (Orientador)
Unidade: ICMC
Subjects: BIOINFORMÁTICA, MUTAÇÃO GENÉTICA, NEOPLASIAS, ONCOLOGIA, APRENDIZADO COMPUTACIONAL, CARCINOGÊNESE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CUTIGI, Jorge Francisco. Computational approaches for the discovery of significant genes in cancer. 2021. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2021. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-18082021-100555/. Acesso em: 05 out. 2024.
APA
Cutigi, J. F. (2021). Computational approaches for the discovery of significant genes in cancer (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-18082021-100555/
NLM
Cutigi JF. Computational approaches for the discovery of significant genes in cancer [Internet]. 2021 ;[citado 2024 out. 05 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-18082021-100555/
Vancouver
Cutigi JF. Computational approaches for the discovery of significant genes in cancer [Internet]. 2021 ;[citado 2024 out. 05 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-18082021-100555/
Trabalho de evento-anais periodico
Topological characterization of cancer driver genes using reactome super pathways networks (2021)
Ramos, Rodrigo Henrique ; Cutigi, Jorge Francisco ; Ferreira, Cynthia de Oliveira Lage ; Simão, Adenilso da Silva
Source: Lecture Notes in Bioinformatics. Conference titles: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Subjects: NEOPLASIAS, BIOINFORMÁTICA, REDES COMPLEXAS, TOPOLOGIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAMOS, Rodrigo Henrique et al. Topological characterization of cancer driver genes using reactome super pathways networks. Lecture Notes in Bioinformatics. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91814-9_3. Acesso em: 05 out. 2024. , 2021
APA
Ramos, R. H., Cutigi, J. F., Ferreira, C. de O. L., & Simão, A. da S. (2021). Topological characterization of cancer driver genes using reactome super pathways networks. Lecture Notes in Bioinformatics. Cham: Springer. doi:10.1007/978-3-030-91814-9_3
NLM
Ramos RH, Cutigi JF, Ferreira C de OL, Simão A da S. Topological characterization of cancer driver genes using reactome super pathways networks [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 26-37.[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_3
Vancouver
Ramos RH, Cutigi JF, Ferreira C de OL, Simão A da S. Topological characterization of cancer driver genes using reactome super pathways networks [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 26-37.[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_3
Artigo de periodico
A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks (2021)
Cutigi, Jorge Francisco ; Evangelista, Adriane Feijó ; Reis, Rui Manuel Vieira ; Simão, Adenilso da Silva
Source: Scientific Reports. Unidade: ICMC
Subjects: BIOINFORMÁTICA, GENÔMICA, NEOPLASIAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CUTIGI, Jorge Francisco et al. A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks. Scientific Reports, v. 11, p. 1-10, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41598-021-02671-8. Acesso em: 05 out. 2024.
APA
Cutigi, J. F., Evangelista, A. F., Reis, R. M. V., & Simão, A. da S. (2021). A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks. Scientific Reports, 11, 1-10. doi:10.1038/s41598-021-02671-8
NLM
Cutigi JF, Evangelista AF, Reis RMV, Simão A da S. A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks [Internet]. Scientific Reports. 2021 ; 11 1-10.[citado 2024 out. 05 ] Available from: https://doi.org/10.1038/s41598-021-02671-8
Vancouver
Cutigi JF, Evangelista AF, Reis RMV, Simão A da S. A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks [Internet]. Scientific Reports. 2021 ; 11 1-10.[citado 2024 out. 05 ] Available from: https://doi.org/10.1038/s41598-021-02671-8

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