Research topics

Research into rare types of cancer

Two visual analytics dashboards are shown, which showcase various visualizations of cancer research data.
Web-based dashboards for visual data analysis in cancer research.

Within the framework of the FFG COIN project VISIOMICS, a web-based platform for integrated exploration of multi-omics and clinical data was created by VRVis in cooperation with St. Anna Kinderkrebsforschung (Paediatric Cancer Research) and other partners in Vienna, which combines visual and automatic data analysis. A research objective is the classification of tumours as well as patient-specific risk stratification and survival prediction. Full integration of multi-scalar genome-wide (multi-omics) data sets, such as cellular features, genomics, gene expression and/or epigenetic data with clinical data, supports exploration into types of cancer in the patient context, therefore helping to improve individual cancer diagnosis precisely in rare types of cancer. While machine and deep learning approaches depend on a larger data stock to learn models, the platform developed by VRVis for visual data exploration represents an alternative especially for rare types of cancer (for which there are only few data sets available) so that researchers can analyse complex relationships and develop new hypotheses.


Improvement in the statistical significance of long-term studies

A snippet of the newspaper Der Standard which reported on VRVis' research work with regards to Visual Analysis of Missing Values in Longitudinal Cohort Study Data.
The daily newspaper "Der Standard" reported on VRVis' research work with regards to data science for medical long-term studies.

Medical long-term studies are often concerned with the relationships between risk factors and incidence of diseases. Large volumes of data are accumulated yet, at the same time, one of the biggest challenges is the lack of data. The dropout of patients and non-recorded values are among the main statistical problems in analysing long-term studies. If they are not correctly handled, they cause a bias in data analysis, thus reducing the statistical significance and therefore the value of the results. Together with epidemiologists at the University of Greifswald, a visual analytics application was developed with VIVID, and this allows missing values to be traced in clinical cohort studies and their character determined. VIVID also integrates methods to replace missing data and to evaluate its validity. VIVID was developed by our scientific partners at the University of Magdeburg in collaboration with VRVis.


Analysis, planning and management of healthcare systems

Ein Dashboard aus Dexhelpp vergleicht Daten von Behandlungsmethoden miteinander.
Vergleich zweier Behandlungsmethoden im Hinblick auf ihre regionale Altersverteilung.

Data from the healthcare system has the potential to be an important decision-making aid. At VRVis, we use visual data analysis and explore new visualization technologies to support the process of data-driven modeling in the healthcare context. We combine decision analysis, data security, data management, statistics, mathematical modeling, simulation and visual analysis. The combination of these methods helps, for example, to analyze and model the spread of diseases in the population, to examine treatment pathways and to investigate the effects of changes in the healthcare system on the basis of computer models. More information about our project DEXHELPP.