Identification of feature genes and pathways for Alzheimer's disease via WGCNA and LASSO regression.
Hongyu SunJin YangXiaohui LiYi LyuZhaomeng XuHui HeXiaomin TongTingyu JiShihan DingChaoli ZhouPengyong HanJinping ZhengPublished in: Frontiers Comput. Neurosci. (2022)
Keyphrases
- gene interactions
- biological entities
- gene expression
- linear regression
- signaling pathways
- biological pathways
- model selection
- gene expression data
- candidate genes
- differentially expressed genes
- disease genes
- differential expression
- ridge regression
- gene regulatory networks
- expression profiles
- microarray data
- complex diseases
- feature selection
- regression model
- gene gene
- metabolic pathways
- gene sets
- dna microarray
- gene networks
- feature set
- microarray
- regression coefficients
- regression problems
- gene function
- regulatory networks
- least squares
- computer aided diagnosis
- systems biology
- gene expression patterns
- related genes
- early diagnosis
- genome wide
- gene selection
- variable selection
- high throughput
- protein protein interaction networks
- cross validation
- biological processes
- mass spectrometry