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Medical Systems Biology

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InvariantSVM

Supplementary files for "Constraining classifiers in molecular analysis: invariance and robustness"

Readme

ReadMe file for the supplementary material.

ReadMe

Code

This provides the code used for training the (invariant) SVMs and predicting the class labels of a new matrix in R.

R-Package: invariantSVM

The code is bundled as the R package invariantSVM_1.0.tar.gz. It can be installed from command line by using the command:

R CMD INSTALL invariantSVM_1.0.tar.gz

After the package is loaded into the workspace (by using library(invariantSVM)), automatic parameter tuning in a multi-objective setting can be performed as shown in the following example:

#calculate 10x10 Accuracy on iris dataset over all invariances and R = 2
library(TunePareto)
print(tunePareto(data = as.matrix(iris[1:100, -ncol(iris)]),
labels = iris[1:100,ncol(iris)],
classifier = tunePareto.invariantSVM(), C=1, R = 2,
invariance = c("lin","off","con","off&con","mon"),
objectiveFunctions = list(cvAccuracy(10, 10))))

Datasets

Collection of the analysed artificial datasets.

Artificial dataset

Artificial dataset (with noise)

Fold lists

Collection of the fold lists used for the 10x10 cross-validation experiments.

Fold-Lists (real datasets)

Result files

This file provides the results of the experiments on artificial and transcriptome datasets. The accuracies and the selected number of features (R1-SVM) are given.

Result tables

Latest News

 

Our paper "Analysis, identification and visualization of subgroups in genomics" has been published in Briefings in Bioinformatics.

 

Our paper "A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data" has been published in Advances in Data Analysis and Classification.