Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to...

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Bibliographic Details
Main Authors: Qidong Cai, Boxue He, Pengfei Zhang, Zhenyu Zhao, Xiong Peng, Yuqian Zhang, Hui Xie, Xiang Wang
Format: Article
Language:English
Published: BMC 2020-12-01
Series:Journal of Translational Medicine
Subjects:
Lung adenocarcinoma
Alternative splicing
Random forests
Splicing switch
Metastasis
Prognosis
Online Access:https://doi.org/10.1186/s12967-020-02635-y

Internet

https://doi.org/10.1186/s12967-020-02635-y

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