Multi-OMICS approaches aim on measuring the dynamics of the most important biomolecules (e.g. genes, mRNAs, proteins and metabolites) in order to gain better understanding of the complex regulation of a cell. Combining data of different platforms provide comprehensive insights into biological processes. Furthermore, in the biomedical research such an approach offers great advantages for the identification and characterization of disease-related processes, biomarkers and drug targets. We introduce a miRNA, mRNA, protein data processing workflow as well as strategies and methods to explore and to integrate different OMICS datasets, mainly focusing on miRNA, mRNA and protein analyses. Our strategies include on the one hand a joint classification concept where selected features of all available OMICS datasets are considered to improve classification accuracy with the aim of generating a ‘cross-omics-biomarker-panel’. On the other hand we focus on strategies to reveal the relationship between miRNA, mRNA and proteins preferable measured from the same patient samples in order to analyze translational regulation effects and to gain better biological insights. These strategies include among other things the analyses of effects of miRNA target relations and their influence on the protein level as well as the integration of subcellular localizations and functional annotations. A challenging research question will be the quantitative estimation of miRNA changes on mRNA translation.