Deep learning algorithm reveals two prognostic subtypes in patients with gliomas

BACKGROUND: Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-sensitive subtypes. Herein, an autoencoder-based ap...

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Detalles Bibliográficos
Autores principales: Tian, Jing, Zhu, Mingzhen, Ren, Zijing, Zhao, Qiang, Wang, Puqing, He, Colin K., Zhang, Min, Peng, Xiaochun, Wu, Beilei, Feng, Rujia, Fu, Minglong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552440/
https://www.ncbi.nlm.nih.gov/pubmed/36221066
http://dx.doi.org/10.1186/s12859-022-04970-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552440/
https://www.ncbi.nlm.nih.gov/pubmed/36221066
http://dx.doi.org/10.1186/s12859-022-04970-x

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