Topological characterization of cancer driver genes using reactome super pathways networks (2021)
- Authors:
- USP affiliated authors: FERREIRA, CYNTHIA DE OLIVEIRA LAGE - ICMC ; SIMÃO, ADENILSO DA SILVA - ICMC ; RAMOS, RODRIGO HENRIQUE - ICMC ; CUTIGI, JORGE FRANCISCO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-030-91814-9_3
- Subjects: NEOPLASIAS; BIOINFORMÁTICA; REDES COMPLEXAS; TOPOLOGIA
- Keywords: Cancer drivers genes; Pathways; Protein interaction
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Lecture Notes in Bioinformatics
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 13063, p. 26-37, 2021
- Conference titles: Brazilian Symposium on Bioinformatics - BSB
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 30 set. 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 set. 30 ] 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 set. 30 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_3 - Human protein-protein interaction networks: a topological comparison review
- Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery
- The survival rate among unvaccinated, first dose, and second dose Brazilian hospitalized and ICU COVID patients by age group
- Analyzing different cancer mutation data sets from breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), and prostate adenocarcinoma (PRAD)
- GeNWeMME: a network-based computational method for prioritizing groups of significant related genes in cancer
- Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective
- A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks
- The central role of cancer driver genes in pathway networks according to persistence homology
- ACDBio: the biological data computational analysis group at ICMC/USP, IFSP, and Barretos Cancer Hospital
- Computational approaches for the discovery of significant genes in cancer
Informações sobre o DOI: 10.1007/978-3-030-91814-9_3 (Fonte: oaDOI API)
Download do texto completo
Tipo | Nome | Link | |
---|---|---|---|
3052806.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas