Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data.
Juan Antonio Gómez PulidoJose L. Cerrada-BarriosSebastian Trinidad-AmadoJosé Manuel Lanza-GutiérrezRamón Ángel Fernández DíazBroderick CrawfordRicardo SotoPublished in: BMC Bioinform. (2016)
Keyphrases
- fine grained
- gene expression data
- fitness function
- cancer classification
- gene selection
- evolutionary algorithm
- gene expression analysis
- microarray
- microarray data
- microarray data analysis
- gene expression
- microarray technology
- cancer diagnosis
- gene expression profiling
- biological data
- microarray gene expression data
- molecular biology
- gene expression data analysis
- gene expression datasets
- gene expression profiles
- feature selection
- high throughput
- colon cancer
- systems biology
- dna microarray
- informative genes
- relevant genes
- gene regulatory networks
- high dimensionality
- microarray datasets
- gene expression data sets
- text mining
- data mining
- data sets
- biologically relevant
- protein protein interactions
- computational biology
- high dimensional
- experimental conditions
- gene expression patterns
- high dimensional data
- data analysis
- machine learning
- selected genes