This study evaluates our novel feature extraction and data integration method for the accurate and interpretable classification of biological samples based on their mRNA and miRNA expression profiles...

This study demonstrates how a researcher could use miXGENE to work on a particular set of experiments. Here we present a set of experiments on germ cell tumor data.

Regulatory co-modules represent a possible way to interpret heterogeneous omics data. When dealing with mRNA and miRNA data, we extract and interpret whole sets of miRNAs and their co-regulated genes instead of the individual mRNAs and miRNAs whose expression is available.

Network-constrained forest is our novel method for regularized omics data classification and mining. The method is based on random forest framework, further enriched by prior known omics interactions. This study demonstrates use of our published method. The workflow, we propose, can be duplicated and further employed with user settings within reproducible research paradigm.

This study depicts the possibilities of mining and visualizing omics patterns defined by gene expression data and underlying prior feature interactions. The study implements existing state-of-the-art method of differential pattern search.