SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data

SIMPLE SUMMARY: Robust methods for modelling and estimation of cancer survival could be relevant in understanding and limiting the impact of cancer. This study was aimed at developing an efficient Machine learning (ML) pipeline that could model survival in Lung Adenocarcinoma (LUAD) patients. Image...

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Detalles Bibliográficos
Autores principales: Kalakoti, Yogesh, Yadav, Shashank, Sundar, Durai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269306/
https://www.ncbi.nlm.nih.gov/pubmed/34206288
http://dx.doi.org/10.3390/cancers13133106

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269306/
https://www.ncbi.nlm.nih.gov/pubmed/34206288
http://dx.doi.org/10.3390/cancers13133106

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