Machine Learning Assistants Construct Oxidative Stress-Related Gene Signature and Discover Potential Therapy Targets for Acute Myeloid Leukemia.
Jinhua ZhangZhenfan ChenFang WangYangbo XiYihan HuJun GuoPublished in: Oxidative medicine and cellular longevity (2022)
We use two different machine learning methods to build six oxidative stress-related gene signatures that could assist clinical decisions and identify PLA2G4A as a potential biomarker for AML. Nobiletin, targeting PLA2G4, may provide a third pathway for therapy AML.
Keyphrases
- acute myeloid leukemia
- machine learning
- oxidative stress
- genome wide
- allogeneic hematopoietic stem cell transplantation
- copy number
- artificial intelligence
- ischemia reperfusion injury
- big data
- induced apoptosis
- diabetic rats
- genome wide identification
- stem cells
- cancer therapy
- dna methylation
- mesenchymal stem cells
- signaling pathway