Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery (2020)
- Authors:
- Cutigi, Jorge Francisco - Instituto Federal de Educação, Ciência e Tecnologia São Paulo (IFSP)
- Evangelista, Renato Feijo
- Ramos, Rodrigo Henrique - Instituto Federal de Educação, Ciência e Tecnologia São Paulo (IFSP)
- Ferreira, Cynthia de Oliveira Lage
- Evangelista, Adriane Feijó
- Carvalho, André Carlos Ponce de Leon Ferreira de
- Simão, Adenilso da Silva
- Cutigi, Jorge Francisco - Instituto Federal de Educação, Ciência e Tecnologia São Paulo (IFSP)
- USP affiliated authors: FERREIRA, CYNTHIA DE OLIVEIRA LAGE - ICMC ; CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC ; SIMÃO, ADENILSO DA SILVA - ICMC ; CUTIGI, JORGE FRANCISCO - ICMC ; EVANGELISTA, RENATO FEIJÓ - ICMC ; RAMOS, RODRIGO HENRIQUE - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-030-65775-8_8
- Subjects: BIOINFORMÁTICA; REDES COMPLEXAS; APRENDIZADO COMPUTACIONAL; GENÔMICA; NEOPLASIAS
- Keywords: Cancer bioinformatics; Driver genes; False-positive driver
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Lecture Notes in Bioinformatics
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 12558, p. 81-92, 2020
- 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
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: 27 set. 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 set. 27 ] 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 set. 27 ] Available from: https://doi.org/10.1007/978-3-030-65775-8_8 - Topological characterization of cancer driver genes using reactome super pathways networks
- Human protein-protein interaction networks: a topological comparison review
- 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: 10.1007/978-3-030-65775-8_8 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 | 3014761.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
