Supplementary files for "Constraining classifiers in molecular analysis: invariance and robustness"
ReadMe file for the supplementary material.
This provides the code used for training the (invariant) SVMs and predicting the class labels of a new matrix in R.
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 = 2library(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))))
Collection of the analysed artificial datasets.
Artificial dataset (with noise)
Collection of the fold lists used for the 10x10 cross-validation experiments.
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.
Our first quantum computing paper "Leveraging quantum computing for dynamic analyses of logical networks in systems biology" has been published in Patterns.
Our paper "Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images" has been published in Frontiers in Artificial Intelligence.
"Vaccine Side Effects in Health Care Workers after Vaccination against SARS-CoV-2: Data from TüSeRe:exact Study" has been published in Viruses-Basel.
"PREDICT-juvenile-stroke: PRospective evaluation of a prediction score determining individual clinical outcome three months after ischemic stroke in young adults – a study protocol" has been published in BMC Neurology.
Our paper "Federated Electronic Data Capture (fEDC): Architecture and Prototype" has been accepted for publiaction in the Journal of Biomedical Informatics.
Our paper "Efficient cross-valdation traversals in feature subset selection" has been published in Scientific Reports.
Our paper "CANTATA - prediction of missing links in Boolean networks using genetic programming" has been published in Bioinformatics.
Our paper "Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives" has been published in the Journal of Medical Internet Research mhealth and uhealth.
Our paper "Identification of dynamic driver sets controlling phenotypical landscapes" has been published in the Computational and Structural Biotechnology Journal.
Congratulations to Dr. Silke Werle for winning the 1st Prize with her pitch at the 1. Science Day held by ProTrainU.
Our paper "Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells" has been published in the Computational and Structural Biotechnology Journal.
The position paper "Is there a role for statistics in artificial intelligence" has been published online first in Advances in Data Analysis and Classification.