The impact of omics-centric studies can be greatly increased by communicating the results in the context of public challenges, for example clinical outcomes using real patient data. This involves building statistical models to link omics data to measurements taken outside the typical laboratory setting. Using gold-standard public domain cohorts Omic Analytics can develop models that predict phenotypic or clinically relevant outcomes using your data. Conversely, we can identify the molecular signatures associated with clinical and environmental variables, shedding light on the potential mechanisms linking these domains.
Integration of datasets from multiple Omics technologies can provide the framework for hypothesis generation with a greatly increased level of accuracy and reliability. Omic Analytics can build multi-level models from omics data that represent biomolecular interactions in a specific biological context such as a disease or environmental stress.
Public domain databases such as NCBI's GEO contain an unprecedented amount of omics data from multiple platforms across thousands of experimental contexts. This under-used resource can be interrogated for existing data relevant to any study, providing knowledge at a fraction of the cost. Omic Analytics can search multiple databases of existing omics studies, which can be integrated into the analysis pipeline.