Cargando…
Identifying 8-mRNAsi Based Signature for Predicting Survival in Patients With Head and Neck Squamous Cell Carcinoma via Machine Learning
Cancer stem cells (CSCs) have been characterized by several exclusive features that include differentiation, self-renew, and homeostatic control, which allows tumor maintenance and spread. Recurrence and therapeutic resistance of head and neck squamous cell carcinomas (HNSCC) have been identified to...
Autores principales: | Tian, Yuxi, Wang, Juncheng, Qin, Chao, Zhu, Gangcai, Chen, Xuan, Chen, Zhixiang, Qin, Yuexiang, Wei, Ming, Li, Zhexuan, Zhang, Xin, Lv, Yunxia, Cai, Gengming |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721480/ https://www.ncbi.nlm.nih.gov/pubmed/33329703 http://dx.doi.org/10.3389/fgene.2020.566159 |
Ejemplares similares
-
Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma
por: Wang, Juncheng, et al.
Publicado: (2020) -
Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets
por: Zhang, Mingwei, et al.
Publicado: (2021) -
Development of a 5-mRNAsi-related gene signature to predict the prognosis of colon adenocarcinoma
por: Huang, Haifu, et al.
Publicado: (2023) -
mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes
por: Wang, Canbiao, et al.
Publicado: (2022) -
mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers
por: Zhang, Yitong, et al.
Publicado: (2020)