Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing.
Feng LyuXiao-Ying LiMing-Wei MaMu XieShiyu ShangXueying RenMingzhu LiuJiayan ChenPublished in: Diagnostics (Basel, Switzerland) (2023)
We engineered an original and novel prognostic model based on five gene signatures through TCGA and machine learning, providing new insights into the risk of scarification and survival prediction for PCa patients in clinical practice.
Keyphrases
- machine learning
- single cell
- genome wide
- clinical practice
- end stage renal disease
- prostate cancer
- copy number
- ejection fraction
- newly diagnosed
- artificial intelligence
- chronic kidney disease
- dna methylation
- big data
- prognostic factors
- high throughput
- deep learning
- gene expression
- patient reported outcomes
- patient reported
- genome wide analysis