alizadeh

The sample data for the MCFS-ID algorithm

datasets

MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a
Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data.

Michal Draminski

rmcfs

The MCFS-ID Algorithm for Feature Selection and Interdependency
Discovery

alizadeh function

A 4026 x 62 gene expression data matrix of log-ratio values. The last column
contains the annotations of the 62 samples with respect to the cancer types
C, D, F.

Format

The data are from the lymphoma/leukemia study of A. Alizadeh et al., Nature 403:503-511 (2000), http://llmpp.nih.gov/lymphoma/index.shtml

alizadeh: Loads sample data from a lymphoma/leukemia gene expression study

Description

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Source