Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.
Andy R. EugeneJolanta MasiakBeata EugenePublished in: F1000Research (2018)
Keyphrases
- gene expression
- machine learning
- microarray
- gene expression profiles
- disease progression
- gene expression data
- survival prediction
- microarray data
- acute myeloid
- gene regulation
- gene expression data analysis
- biological processes
- gene expression patterns
- information extraction
- feature selection
- clinical trials
- gene selection
- binding sites
- functional genomics
- dna microarray
- gene expression analysis
- differential expression
- differentially expressed genes
- gene interactions
- ovarian cancer
- analysis of gene expression
- microarray datasets
- regulatory networks
- biological networks
- high throughput
- data mining
- microarray data analysis
- data sets
- prostate cancer
- computational biology
- knowledge discovery