An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories.
Rameswar DebnathTakio KuritaPublished in: Biosyst. (2010)
Keyphrases
- gene selection
- microarray data
- error bounds
- cancer classification
- support vector machine svm
- feature selection
- relevant genes
- cancer diagnosis
- dna microarray data
- microarray
- microarray data analysis
- dna microarray
- microarray datasets
- informative genes
- gene expression profiles
- ovarian cancer
- small sample size
- colon cancer
- gene expression data
- feature ranking
- gene expression
- microarray classification
- support vector machine
- microarray analysis
- small number of samples
- biologically relevant
- gene expression datasets
- support vector
- cluster analysis
- data sets
- pattern recognition
- high dimensional
- selected genes
- high throughput
- machine learning methods
- svm classifier
- decision trees
- machine learning
- gene expression analysis
- high dimensionality
- worst case
- supervised learning
- knn
- feature space
- expectation maximization
- dimensionality reduction
- differentially expressed genes