Bioconductor-mirror/cancerclass: Development and validation of diagnostic tests from high-dimensional molecular data

The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements.

Getting started

Package details

AuthorJan Budczies, Daniel Kosztyla
Bioconductor views Cancer Classification Microarray Visualization
MaintainerDaniel Kosztyla <[email protected]>
LicenseGPL 3
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
Bioconductor-mirror/cancerclass documentation built on June 1, 2017, 5:25 a.m.