An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.
Arthur L. HsuSen-Lin TangSaman K. HalgamugePublished in: Bioinform. (2003)
Keyphrases
- microarray data
- gene selection
- microarray
- relevant genes
- cancer classification
- gene expression
- dna microarray
- gene expression profiles
- biologically significant
- gene expression data
- gene sets
- microarray technology
- gene expression data sets
- meta analysis
- microarray gene expression data
- informative genes
- dna microarray data
- gene networks
- microarray datasets
- gene clusters
- feature selection
- biologically meaningful
- cancer diagnosis
- selected genes
- differentially expressed genes
- colon cancer
- data sets
- microarray data analysis
- cluster analysis
- unsupervised learning
- biologically relevant
- ovarian cancer
- analysis of gene expression
- microarray gene expression
- high dimensional
- biological data
- saccharomyces cerevisiae
- regulatory networks
- gene ontology
- microarray analysis
- data mining
- gene expression patterns
- gene expression microarray data
- small number of samples
- high dimensionality
- high throughput
- supervised learning
- knowledge discovery
- experimental conditions
- gene expression analysis
- molecular biology
- random forest
- gene regulatory networks