MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection

Show full item record



Permalink

http://hdl.handle.net/10138/224229

Citation

BMC Bioinformatics. 2017 Sep 13;18(Suppl 10):393

Title: MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection
Author: He, Chen; Micallef, Luana; Tanoli, Zia-ur-Rehman; Kaski, Samuel; Aittokallio, Tero; Jacucci, Giulio
Publisher: BioMed Central
Date: 2017-09-13
URI: http://hdl.handle.net/10138/224229
Abstract: Abstract Background Dispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. 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. 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.
Subject: Interactive visualization
Uncertainty visualization
Multiple datasets


Files in this item

Total number of downloads: Loading...

Files Size Format View
12859_2017_Article_1785.pdf 1.413Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record