MediSyn: Uncertainty-aware Visualization of Multiple Biomedical Datasets to Support Drug Treatment Selection
Research output: Contribution to journal › Article › Scientific › peer-review
|Publication status||Published - 13 Sep 2017|
|MoE publication type||A1 Journal article-refereed|
|Event||Symposium on Biological Data Visualization - Prague, Czech Republic|
Duration: 24 Jul 2017 → 24 Jul 2017
- University of Helsinki
Results: To investigate the requirements and challenges of uncertainty-aware visualizations of linked data, we developed MediSyn, a system that synthesizes medical datasets to support drug treatment selection. It uses a matrix-based layout to visually link drugs, targets (e.g., mutations), and tumor types. Data uncertainties are salient in MediSyn; for example, (i) missing data are exposed in the matrix view of drug-target relations; (ii) inconsistencies between datasets are shown via overlaid layers; and (iii) data credibility is conveyed through links to data provenance.
Conclusions: Through the synthesis of two manually curated datasets, cancer treatment biomarkers and drug-target bioactivities, a use case shows how MediSyn effectively supports the discovery of drug-repurposing opportunities. A study with six domain experts indicated that MediSyn benefited the drug selection and data inconsistency discovery. Though linked publication sources supported user exploration for further information, the causes of inconsistencies were not easy to find. Additionally, MediSyn could embrace more patient data to increase its informativeness. We derive design implications from the findings.
- Interactive visualization, Uncertainty visualization, Multiple datasets