A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters.
Matthias GerstgrasserSarah NichollsMichael StoutKatherine SmartChris PowellTheodore KypraiosDov J. StekelPublished in: J. Bioinform. Comput. Biol. (2016)
Keyphrases
- microarray data
- gene expression
- microarray
- biologically meaningful
- gene expression data
- high throughput
- gene selection
- parameter estimation
- data sets
- microarray data analysis
- meta analysis
- feature selection
- small number of samples
- cluster analysis
- biological data
- differentially expressed genes
- genome wide
- microarray gene expression data
- high dimensional
- gene networks
- dna microarray data
- microarray classification
- dna microarray
- biologically relevant
- gene clusters
- gene expression patterns
- analysis of gene expression
- data mining
- biological networks
- gene expression analysis
- cancer classification
- gene sets
- gene expression profiles
- biological processes
- gene ontology
- sequence data
- statistically significant
- maximum likelihood
- input data