Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease.
Song CuiQiang WuJames WestJiangping BaiPublished in: PLoS Comput. Biol. (2019)
Keyphrases
- microarray
- expression profiles
- machine learning
- gene expression levels
- gene expression
- gene sets
- differentially expressed genes
- gene expression data
- gene expression profiles
- microarray data
- high throughput
- gene gene
- gene networks
- differential expression
- expression patterns
- cell cycle
- meta analysis
- differentially expressed
- gene selection
- experimental conditions
- gene ontology
- dna microarray
- gene function
- microarray data analysis
- molecular biology
- gene expression profiling
- gene expression datasets
- microarray datasets
- high dimensionality
- yeast cell cycle
- cancer classification
- protein protein interactions
- selected genes
- gene expression analysis
- feature selection
- microarray analysis
- genomic data
- regulatory networks
- related genes
- gene expression data analysis
- gene expression data sets
- colon cancer
- biological networks
- gene expression microarray data
- microarray images
- microarray technology
- biological processes
- transcription factors
- sequence data
- biological data
- text mining