Cargando…
A deep learning-based framework for lung cancer survival analysis with biomarker interpretation
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and treatment management. RESULTS: In...
Autores principales: | Cui, Lei, Li, Hansheng, Hui, Wenli, Chen, Sitong, Yang, Lin, Kang, Yuxin, Bo, Qirong, Feng, Jun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079513/ https://www.ncbi.nlm.nih.gov/pubmed/32183709 http://dx.doi.org/10.1186/s12859-020-3431-z |
Ejemplares similares
-
Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis
por: Yan, Jiawei, et al.
Publicado: (2023) -
An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification
por: Probst, Daniel
Publicado: (2023) -
Interpretable deep learning survival predictive tool for small cell lung cancer
por: Zhang, Dongrui, et al.
Publicado: (2023) -
The Public Health 12 framework: interpreting the ‘Meadows 12 places to act in a system’ for use in public health
por: Bolton, Kristy A., et al.
Publicado: (2022) -
DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis
por: Zhao, Lianhe, et al.
Publicado: (2021)