Combining multiple hypothesis testing and affinity propagation clustering leads to accurate, robust and sample size independent classification on gene expression data.
Argiris SakellariouDespina SanoudouGeorge M. SpyrouPublished in: BMC Bioinform. (2012)
Keyphrases
- sample size
- hypothesis testing
- gene expression data
- combining multiple
- statistical tests
- small sample size
- confidence intervals
- hypothesis tests
- microarray data
- high dimensionality
- gene expression
- model selection
- microarray
- gene expression profiles
- feature selection
- data sets
- random sampling
- microarray datasets
- feature space
- classification accuracy
- gene selection
- pattern recognition
- machine learning
- hypothesis test
- training data
- high dimensional
- feature extraction
- class labels
- cluster ensemble
- support vector machine svm
- support vector machine
- linear discriminant analysis
- face recognition
- statistical significance
- decision trees
- cluster analysis
- high dimensional data
- text classification