Discovering Statistically Significant Clusters by Using Iterative Genetic Algorithms in Gene Expression Data.
Hua-Sheng ChiuHan-Yu ChuangHuai-Kuang TsaiTao-Wei HuangCheng-Yan KaoPublished in: METMBS (2004)
Keyphrases
- statistically significant
- gene expression data
- analysis of gene expression data
- microarray
- gene expression
- biologically significant
- microarray data
- gene expression analysis
- control group
- high dimensionality
- gene clusters
- statistical significance
- feature selection
- learning styles
- clustering algorithm
- highly correlated
- gene selection
- gene regulatory networks
- gene expression datasets
- high dimensional
- gene expression data analysis
- microarray gene expression data
- simultaneous clustering
- cancer classification
- high dimensional data
- statistical tests
- data sets
- gene expression profiling
- dna microarray
- regulatory networks
- gene expression profiles
- hierarchical clustering
- neural network
- pearson correlation
- microarray datasets
- cluster analysis
- machine learning
- high throughput
- overlapping clusters
- self organizing maps