Deep learning algorithm reveals two prognostic subtypes in patients with gliomas

Deep learning algorithm reveals two prognostic subtypes in patients with gliomas

Abstract 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-...

Full description

Bibliographic Details
Main Authors: Jing Tian, Mingzhen Zhu, Zijing Ren, Qiang Zhao, Puqing Wang, Colin K. He, Min Zhang, Xiaochun Peng, Beilei Wu, Rujia Feng, Minglong Fu
Format: Article
Language:English
Published: BMC 2022-10-01
Series:BMC Bioinformatics
Subjects:
Autoencoder-based approach
Support vector machine
Survival-sensitive subtypes
Multi-omics data
Glutathione metabolism pathway
Online Access:https://doi.org/10.1186/s12859-022-04970-x

Similar Items

Similar Items