A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery.
Hao WangZhaoyue ZhangHaicheng LiJinzhao LiHanshuang LiMingzhu LiuPengfei LiangQilemuge XiYongqiang XingLei YangYongchun ZuoPublished in: Cell & bioscience (2023)
Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.
Keyphrases
- early onset
- risk assessment
- machine learning
- single cell
- rna seq
- late onset
- decision making
- human health
- high throughput
- genome wide
- heavy metals
- artificial intelligence
- oxidative stress
- big data
- small molecule
- deep learning
- dna methylation
- convolutional neural network
- genome wide identification
- neural network
- transcription factor
- climate change
- monte carlo