Cargando…
Decentralized Learning Framework of Meta-Survival Analysis for Developing Robust Prognostic Signatures
PURPOSE: A significant hurdle in developing reliable gene expression–based prognostic models has been the limited sample size, which can cause overfitting and false discovery. Combining data from multiple studies can enhance statistical power and reduce spurious findings, but how to address the biol...
Autores principales: | Cui, Yi, Li, Bailiang, Li, Ruijiang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society of Clinical Oncology
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873986/ https://www.ncbi.nlm.nih.gov/pubmed/30657395 http://dx.doi.org/10.1200/CCI.17.00077 |
Ejemplares similares
-
An improved framework to predict river flow time series data
por: Nazir, Hafiza Mamona, et al.
Publicado: (2019) -
A data integration framework for spatial interpolation of temperature observations using climate model data
por: Economou, Theo, et al.
Publicado: (2023) -
Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis
por: Chen, Yijiang, et al.
Publicado: (2020) -
Survival analysis in clinical trials: Basics and must know areas
por: Singh, Ritesh, et al.
Publicado: (2011) -
OSPred Tool: A Digital Health Aid for Rapid Predictive Analysis of Correlations Between Early End Points and Overall Survival in Non–Small-Cell Lung Cancer Clinical Trials
por: Shameer, Khader, et al.
Publicado: (2022)