Machine learning identifies stemness features associated with oncogenic dedifferentiation (2018)
- Authors:
- USP affiliated authors: NOUSHMEHR, HOUTAN - FMRP ; CARLOTTI JUNIOR, CARLOS GILBERTO - FMRP ; SANTOS, JOSÉ SEBASTIÃO DOS - FMRP ; KEMP, RAFAEL - FMRP ; SANKARANKUTTY, AJITH KUMAR - FMRP ; TIRAPELLI, DANIELA PRETTI DA CUNHA - FMRP
- Unidade: FMRP
- DOI: 10.1016/j.cell.2018.03.034
- Subjects: GENÔMICA; NEOPLASIAS; CÉLULAS-TRONCO; APRENDIZADO COMPUTACIONAL; ALGORITMOS
- Keywords: The Cancer Genome Atlas; Cancer stem cells; Dedifferentiation; Epigenomic; Genomic; Machine learning; Pan-cancer; Stemness
- Agências de fomento:
- Finandiado pelo NIH
- Financiado pelo National Cancer Institute, Cairo University
- Finandiado pelo National Institute of General Medical Sciences
- Financiado pela Mary K. Chapman Foundation
- Financido pelo Cancer Prevention and Research Institute of Texas
- Financiado pela Michael and Susan Dell Foundation
- Financiado pelo FNP
- Financiado pelo Henry Ford Cancer Institute’s Early Career Investigator Award
- Financiado pela FAPESP
- Financiado pelo Henry Ford Hospital
- Financiado pelo Spanish Institute of Health Carlos III
- Language: Inglês
- Imprenta:
- Source:
- Status:
- Artigo possui acesso gratuito no site do editor (Bronze Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
MALTA, Tathiane M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell, v. 173, n. 2, p. 338-354.e1-e5, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.cell.2018.03.034. Acesso em: 16 abr. 2026. -
APA
Malta, T. M., Noushmehr, H., Carlotti Júnior, C. G., Santos, J. S. dos, Kemp, R., Sankarankutty, A. K., & Tirapelli, D. P. da C. (2018). Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell, 173( 2), 338-354.e1-e5. doi:10.1016/j.cell.2018.03.034 -
NLM
Malta TM, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankutty AK, Tirapelli DP da C. Machine learning identifies stemness features associated with oncogenic dedifferentiation [Internet]. Cell. 2018 ; 173( 2): 338-354.e1-e5.[citado 2026 abr. 16 ] Available from: https://doi.org/10.1016/j.cell.2018.03.034 -
Vancouver
Malta TM, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankutty AK, Tirapelli DP da C. Machine learning identifies stemness features associated with oncogenic dedifferentiation [Internet]. Cell. 2018 ; 173( 2): 338-354.e1-e5.[citado 2026 abr. 16 ] Available from: https://doi.org/10.1016/j.cell.2018.03.034 - Integrated genomic characterization of oesophageal carcinoma
- Comprehensive analysis of alternative splicing across tumors from 8,705 patients
- Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines
- Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers
- Oncogenic signaling pathways in the cancer genome atlas
- Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer
- Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context
- Comprehensive molecular characterization of the hippo signaling pathway in cancer
- Perspective on oncogenic processes at the end of the beginning of cancer genomics
- Pathogenic germline variants in 10,389 adult cancers
Informações sobre a disponibilidade de versões do artigo em acesso aberto coletadas automaticamente via oaDOI API (Unpaywall).
Por se tratar de integração com serviço externo, podem existir diferentes versões do trabalho (como preprints ou postprints), que podem diferir da versão publicada.
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
| 002937364.pdf | Direct link |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas