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

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http://hdl.handle.net/10138/225146

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He , C , Micallef , L , Tanoli , Z-R , Kaski , S , Aittokallio , T & Jacucci , G 2017 , ' MediSyn : uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection ' , BMC Bioinformatics , vol. 18 , 393 . https://doi.org/10.1186/s12859-017-1785-7

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
Contributor: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Department of Computer Science
Date: 2017
Language: eng
Number of pages: 12
Belongs to series: BMC Bioinformatics
ISSN: 1471-2105
URI: http://hdl.handle.net/10138/225146
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
DISCOVERY
INFORMATION
PLATFORM
DESIGN
3111 Biomedicine
1182 Biochemistry, cell and molecular biology
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