person("Alicja", "Gosiewska", role = "aut"),
person("Adam", "Izdebski", role = "aut"),
person("Dariusz", "Komosinski", role = "ctb"))
Suggests: knitr, rmarkdown, randomForest, e1071, xgboost, ranger, nnet,

person("Alicja", "Gosiewska", role = "aut",

Suggests: DALEX, knitr, rmarkdown, randomForest, e1071, ranger, nnet,

Title: A copy number and expression-based classifier for breast
Maintainer: Oscar M. Rueda <Oscar.Rueda@cruk.cam.ac.uk>
Description: iC10 implements the classifier described in the METABRIC paper 'The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups' (Curtis et al., Nature 2012). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.

Description: iC10 implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.

Title: A copy number and expression-based classifier for breast tumours

Title: A Copy Number and Expression-Based Classifier for Breast Tumours
Description: Implementation of the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.

Title: Training datasets for iC10 package.
Description: Training datasets for iC10; which implements the classifier described in the METABRIC paper 'The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups' (Curtis et al., Nature 2012). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.