Cargando…
Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data
BACKGROUND: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause...
Autores principales: | Hao, Jie, Kim, Youngsoon, Mallavarapu, Tejaswini, Oh, Jung Hun, Kang, Mingon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927105/ https://www.ncbi.nlm.nih.gov/pubmed/31865908 http://dx.doi.org/10.1186/s12920-019-0624-2 |
Ejemplares similares
-
PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data
por: Hao, Jie, et al.
Publicado: (2018) -
PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma
por: Oh, Jung Hun, et al.
Publicado: (2021) -
Editorial of Special Issue “Deep Learning and Machine Learning in Bioinformatics”
por: Kang, Mingon, et al.
Publicado: (2022) -
Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data
por: Jo, Jongkwon, et al.
Publicado: (2022) -
Correcting gradient-based interpretations of deep neural networks for genomics
por: Majdandzic, Antonio, et al.
Publicado: (2023)