Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma

Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma

Abstract Background A plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study was to identify the optimal biomarkers for ESCC using...

Full description

Bibliographic Details
Main Authors: Meng-Xiang Li, Xiao-Meng Sun, Wei-Gang Cheng, Hao-Jie Ruan, Ke Liu, Pan Chen, Hai-Jun Xu, She-Gan Gao, Xiao-Shan Feng, Yi-Jun Qi
Format: Article
Language:English
Published: BMC 2021-08-01
Series:BMC Cancer
Subjects:
Esophageal squamous cell carcinoma
Stratifin
Machine learning
Support vector machine
Random forest
Logical regression
Online Access:https://doi.org/10.1186/s12885-021-08647-1

Internet

https://doi.org/10.1186/s12885-021-08647-1

Similar Items