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

Chen He, Luana Micallef, Zia-ur-Rehman Tanoli, Samuel Kaski, Tero Aittokallio, Giulio Jacucci
doi: https://doi.org/10.1101/165878
Chen He
1Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, 00560, Helsinki, Finland
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  • For correspondence: chen.he@helsinki.fi
Luana Micallef
2Department of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
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Zia-ur-Rehman Tanoli
3Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland
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Samuel Kaski
2Department of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
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Tero Aittokallio
3Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland
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Giulio Jacucci
1Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, 00560, Helsinki, Finland
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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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 20, 2017.
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MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection
Chen He, Luana Micallef, Zia-ur-Rehman Tanoli, Samuel Kaski, Tero Aittokallio, Giulio Jacucci
bioRxiv 165878; doi: https://doi.org/10.1101/165878
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MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection
Chen He, Luana Micallef, Zia-ur-Rehman Tanoli, Samuel Kaski, Tero Aittokallio, Giulio Jacucci
bioRxiv 165878; doi: https://doi.org/10.1101/165878

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