An entropy-based classification of breast cancerous genes using microarray data.
Mausami MondalRahul SemwalUtkarsh RajImlimaong AierPritish Kumar VaradwajPublished in: Neural Comput. Appl. (2020)
Keyphrases
- microarray data
- microarray data analysis
- gene selection
- microarray datasets
- cancer classification
- dna microarray data
- informative genes
- feature selection
- gene expression profiles
- gene expression
- relevant genes
- microarray
- small number of samples
- gene expression data
- microarray gene expression data
- dna microarray
- differentially expressed genes
- colon cancer
- microarray analysis
- gene sets
- gene expression microarray data
- meta analysis
- gene networks
- high dimensional
- biological data
- selected genes
- gene expression data sets
- ovarian cancer
- biologically meaningful
- cluster analysis
- data sets
- biologically significant
- cancer diagnosis
- gene expression patterns
- gene function prediction
- saccharomyces cerevisiae
- gene clusters
- support vector machine svm
- gene expression datasets
- random forest
- decision trees
- supervised learning
- feature space
- gene expression analysis
- feature extraction
- gene expression levels
- high dimensionality
- support vector machine
- unsupervised learning
- biologically relevant
- semi supervised
- feature ranking
- microarray technology
- machine learning