Partial least squares and logistic regression random-effects estimates for gene selection in supervised classification of gene expression data.
Arief GusnantoAlexander PlonerFarag ShuweihdiYudi PawitanPublished in: J. Biomed. Informatics (2013)
Keyphrases
- logistic regression
- partial least squares
- gene selection
- gene expression data
- microarray
- microarray data
- gene expression
- cancer classification
- dimension reduction
- dna microarray
- discriminant analysis
- gene expression profiles
- support vector
- high dimensional data
- high dimensional
- high dimensionality
- naive bayes
- decision trees
- data sets
- feature selection
- chi square
- loss function
- random forests
- unsupervised learning
- supervised learning
- fold cross validation
- ls svm
- feature ranking
- factor analysis
- reinforcement learning
- input data
- cluster analysis
- pairwise
- cross validation
- machine learning