Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer.
Ricardo de Matos SimoesConstantine MitsiadesKate E. WilliamsonFrank Emmert-StreibPublished in: BIBM (2015)
Keyphrases
- machine learning
- decision trees
- microarray
- outcome prediction
- cancer diagnosis
- cancer classification
- gene expression profiles
- training set
- pairwise
- text classification
- magnetic resonance spectroscopy
- neural network
- gene prediction
- colon cancer
- microarray analysis
- gene selection
- network model
- high throughput
- complex networks
- classification accuracy
- data analysis
- feature extraction