Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients’ prognosis and immunotherapy response-Reference-Cited by-同舟云学术

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients’ prognosis and immunotherapy response

Published:2023-05-23 Issue:10 Volume:15 Page:4051-4070
ISSN:1945-4589
Container-title:Aging
language:en
Short-container-title:Aging

Author:

Li Chuanyu¹,Liu Wangrui¹²,Liu Chengming³,Luo Qisheng¹,Luo Kunxiang¹,Wei Cuicui⁴,Li Xueyu¹,Qin Jiancheng¹,Zheng Chuanhua¹,Lan Chuanliu¹,Wei Shiyin¹,Tan Rong¹,Chen Jiaxing¹,Chen Yuanbiao¹,Huang Huadong¹,Zhang Gaolian⁵,Huang Haineng¹,Wang Xiangyu⁶

Affiliation:

1. Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China

2. Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

3. Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu, China

4. Department of Outpatient, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China

5. Department of Neurosurgery, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China

6. Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou 510632, Guangdong Province, China

Publisher

Impact Journals, LLC

Subject

Cell Biology,Aging

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