Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms.
Wei LuXiang PanSiqi DaiDongliang FuMaxwell HwangYingshuang ZhuLina ZhangJingsun WeiXiangxing KongJun LiQian XiaoKe-Feng DingPublished in: Journal of oncology (2021)
We identified 479 stage II colorectal cancer recurrence associated genes by microarray meta-analysis. The random survival forest model which was based on the recurrence associated gene signature could strongly predict the recurrence risk of stage II colorectal cancer patients.
Keyphrases
- systematic review
- free survival
- machine learning
- genome wide
- end stage renal disease
- bioinformatics analysis
- meta analyses
- genome wide identification
- newly diagnosed
- chronic kidney disease
- prognostic factors
- climate change
- randomized controlled trial
- peritoneal dialysis
- gene expression
- big data
- artificial intelligence
- copy number
- case control
- genome wide analysis
- patient reported outcomes