CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer.
Thomas ShermanLuciane T. KagoharaRaymon CaoRaymond ChengMatthew SatrianoMichael ConsidineGabriel KrigsfeldRuchira RanaweeraYong TangSandra A. JablonskiGenevieve L. Stein-O'BrienDaria A. GaykalovaLouis M. WeinerChristine H. ChungElana J. FertigPublished in: PLoS Comput. Biol. (2019)
Keyphrases
- gene expression data
- statistical modeling
- gene selection
- microarray
- gene expression profiles
- cancer diagnosis
- gene expression
- tissue samples
- cancer classification
- dna microarray
- gene expression datasets
- gene expression data sets
- gene expression data analysis
- gene expression profiling
- microarray data
- cancer datasets
- statistical models
- data sets
- colon cancer
- biologically significant
- gene expression analysis
- gene regulatory networks
- analysis of gene expression data
- high throughput
- feature selection
- microarray gene expression data
- high dimensionality
- high dimensional
- tumor classification
- analysis of gene expression
- microarray technology
- gene regulation
- genomic data
- gene networks
- microarray datasets
- random forest
- breast cancer
- high dimensional data
- gene expression patterns
- experimental conditions
- nearest neighbor
- image processing
- computer vision