Cargando…
Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis, Cox regression, and L1-LASSO penalization
PURPOSE: This study focused on identification of long non-coding RNAs (lncRNAs) for prognosis prediction of glioblastoma (GBM) through weighted gene co-expression network analysis (WGCNA) and L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model. MA...
Autores principales: | Liang, Ruqing, Zhi, Yaqin, Zheng, Guizhi, Zhang, Bin, Zhu, Hua, Wang, Meng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306053/ https://www.ncbi.nlm.nih.gov/pubmed/30613154 http://dx.doi.org/10.2147/OTT.S171957 |
Ejemplares similares
-
The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO
por: Akkol, Suna
Publicado: (2018) -
A comprehensive analysis of prognosis prediction models based on pathway-level, gene-level and clinical information for glioblastoma
por: Liang, Ruqing, et al.
Publicado: (2018) -
LASSO type penalized spline regression for binary data
por: Mullah, Muhammad Abu Shadeque, et al.
Publicado: (2021) -
Gene set selection via LASSO penalized regression (SLPR)
por: Frost, H. Robert, et al.
Publicado: (2017) -
Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening
por: Jardillier, Rémy, et al.
Publicado: (2022)