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Motivation

Due to the establishment of cost-effective, high-throughput technologies for the automated measurement of high-dimensional molecular data, bioinformatics has developed rapidly as a new scientific discipline within the last two decades. Inside the bioinformatics discipline, numerous tools for data analysis, data visualization, and data integration for diverse biomedical information have been developed.

Using statistical modeling of gene expression data, methylation data, and next-generation high-throughput sequencing data, one is able to connect molecular data to, for example, clinical markers. This information can be used to assist in decisions for individual therapies for patients.

Additionally, numerous algorithms in bioinformatics enable us to investigate the underlying connections of differentially-expressed genes with the help of analyses of signal pathways and networks. Thus, we are able to characterize the genes at a higher abstraction/functional/informational level, e.g. within enriched pathways or gene ontologies.

Single biological levels (transcriptome, proteome, etc.) usually do not act independently of each other, but act in a systemic way forming a complex system. Therefore, one needs bioinformatic tools to allow the biological levels to be connected. While statistical approaches do exist to perform such data integration, most often these approaches have to be adapted to every single application, or completely new tools may even need to be developed. This is often the only way that one is able to find new interacting gene clusters.