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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: 14 ago. 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 ago. 14 ] 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 ago. 14 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-18082021-100555/
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: 14 ago. 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 ago. 14 ] 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 ago. 14 ] Available from: https://doi.org/10.1038/s41598-021-02671-8
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: 14 ago. 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 ago. 14 ] 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 ago. 14 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_3
Trabalho de evento-anais periodico
Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery (2020)
Cutigi, Jorge Francisco ; Evangelista, Renato Feijo ; Ramos, Rodrigo Henrique ; Ferreira, Cynthia de Oliveira Lage ; Evangelista, Adriane Feijó ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Simão, Adenilso da Silva
Source: Lecture Notes in Bioinformatics. Conference titles: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Subjects: BIOINFORMÁTICA, REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, GENÔMICA, NEOPLASIAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CUTIGI, Jorge Francisco et al. Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery. Lecture Notes in Bioinformatics. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-65775-8_8. Acesso em: 14 ago. 2024. , 2020
APA
Cutigi, J. F., Evangelista, R. F., Ramos, R. H., Ferreira, C. de O. L., Evangelista, A. F., Carvalho, A. C. P. de L. F. de, & Simão, A. da S. (2020). Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery. Lecture Notes in Bioinformatics. Cham: Springer. doi:10.1007/978-3-030-65775-8_8
NLM
Cutigi JF, Evangelista RF, Ramos RH, Ferreira C de OL, Evangelista AF, Carvalho ACP de LF de, Simão A da S. Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery [Internet]. Lecture Notes in Bioinformatics. 2020 ; 12558 81-92.[citado 2024 ago. 14 ] Available from: https://doi.org/10.1007/978-3-030-65775-8_8
Vancouver
Cutigi JF, Evangelista RF, Ramos RH, Ferreira C de OL, Evangelista AF, Carvalho ACP de LF de, Simão A da S. Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery [Internet]. Lecture Notes in Bioinformatics. 2020 ; 12558 81-92.[citado 2024 ago. 14 ] Available from: https://doi.org/10.1007/978-3-030-65775-8_8
Artigo de periodico
Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective (2020)
Cutigi, Jorge Francisco ; Evangelista, Adriane Feijó ; Simão, Adenilso da Silva
Source: Journal of Bioinformatics and Computational Biology. Unidade: ICMC
Subjects: NEOPLASIAS, MUTAÇÃO GENÉTICA, BIOINFORMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CUTIGI, Jorge Francisco e EVANGELISTA, Adriane Feijó e SIMÃO, Adenilso da Silva. Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective. Journal of Bioinformatics and Computational Biology, v. 18, n. 3, p. 2050016-1-2050016-32, 2020Tradução . . Disponível em: https://doi.org/10.1142/S021972002050016X. Acesso em: 14 ago. 2024.
APA
Cutigi, J. F., Evangelista, A. F., & Simão, A. da S. (2020). Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective. Journal of Bioinformatics and Computational Biology, 18( 3), 2050016-1-2050016-32. doi:10.1142/S021972002050016X
NLM
Cutigi JF, Evangelista AF, Simão A da S. Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective [Internet]. Journal of Bioinformatics and Computational Biology. 2020 ; 18( 3): 2050016-1-2050016-32.[citado 2024 ago. 14 ] Available from: https://doi.org/10.1142/S021972002050016X
Vancouver
Cutigi JF, Evangelista AF, Simão A da S. Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective [Internet]. Journal of Bioinformatics and Computational Biology. 2020 ; 18( 3): 2050016-1-2050016-32.[citado 2024 ago. 14 ] Available from: https://doi.org/10.1142/S021972002050016X
Trabalho de evento-anais periodico
GeNWeMME: a network-based computational method for prioritizing groups of significant related genes in cancer (2020)
Cutigi, Jorge Francisco ; Evangelista, Adriane Feijó ; Simão, Adenilso da Silva
Source: Lecture Notes in Bioinformatics - LNBI. Conference titles: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Subjects: NEOPLASIAS, MUTAÇÃO, GENÔMICA, BIOINFORMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CUTIGI, Jorge Francisco e EVANGELISTA, Adriane Feijó e SIMÃO, Adenilso da Silva. GeNWeMME: a network-based computational method for prioritizing groups of significant related genes in cancer. Lecture Notes in Bioinformatics - LNBI. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-46417-2_3. Acesso em: 14 ago. 2024. , 2020
APA
Cutigi, J. F., Evangelista, A. F., & Simão, A. da S. (2020). GeNWeMME: a network-based computational method for prioritizing groups of significant related genes in cancer. Lecture Notes in Bioinformatics - LNBI. Cham: Springer. doi:10.1007/978-3-030-46417-2_3
NLM
Cutigi JF, Evangelista AF, Simão A da S. GeNWeMME: a network-based computational method for prioritizing groups of significant related genes in cancer [Internet]. Lecture Notes in Bioinformatics - LNBI. 2020 ; 11347 29-40.[citado 2024 ago. 14 ] Available from: https://doi.org/10.1007/978-3-030-46417-2_3
Vancouver
Cutigi JF, Evangelista AF, Simão A da S. GeNWeMME: a network-based computational method for prioritizing groups of significant related genes in cancer [Internet]. Lecture Notes in Bioinformatics - LNBI. 2020 ; 11347 29-40.[citado 2024 ago. 14 ] Available from: https://doi.org/10.1007/978-3-030-46417-2_3

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