![]() 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. It uses a matrix-based layout to visually link drugs, targets (e.g., mutations), and tumor types. 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. Devising a visualization that synthesizes multiple sources in such a way that links between data sources are transparent, and uncertainties, such as data conflicts, are salient is challenging. In addition, potential errors in the data are difficult to detect in their free formats. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. ![]() Dispersed biomedical databases limit user exploration to generate structured knowledge.
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