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2017 ; 18
(Suppl 10
): 393
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MediSyn: uncertainty-aware visualization of multiple biomedical datasets to
support drug treatment selection
#MMPMID28929971
He C
; Micallef L
; Tanoli ZU
; Kaski S
; Aittokallio T
; Jacucci G
BMC Bioinformatics
2017[Sep]; 18
(Suppl 10
): 393
PMID28929971
show ga
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.
free
free
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