Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
Open accessAbstract
Whether AI explanations can help users achieve specific tasks efficiently (i.e., usable explanations) is significantly influenced by their visual presentation. While many techniques exist to generate explanations, it remains unclear how to select and visually present AI explanations based on the characteristics of domain users. This paper aims to understand this question through a multidisciplinary design study for a specific problem: explaining graph neural network (GNN) predictions to domain experts in drug repurposing, i.e., reuse of existing drugs for new diseases. Building on the nested design model of visualization, we incorporate XAI design considerations from a literature review and from our collaborators' feedback into the design process. Specifically, we discuss XAI-related design considerations for usable visual explanations at each design layer: target user, usage context, domain explanation, and XAI goal at the domain layer; format, granularity, and operation of explanations at the abstraction layer; encodings and interactions at the visualization layer; and XAI and rendering algorithm at the algorithm layer. We present how the extended nested model motivates and informs the design of DrugExplorer, an XAI tool for drug repurposing. Based on our domain characterization, DrugExplorer provides path-based explanations and presents them both as individual paths and meta-paths for two key XAI operations, why and what else. DrugExplorer offers a novel visualization design called MetaMatrix with a set of interactions to help domain users organize and compare explanation paths at different levels of granularity to generate domain-meaningful insights. We demonstrate the effectiveness of the selected visual presentation and DrugExplorer as a whole via a usage scenario, a user study, and expert interviews. From these evaluations, we derive insightful observations and reflections that can inform the design of XAI visualizations for other scientific applications.
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Path-based reasoning enhanced clinician comprehension and satisfaction.
“Among a range of designs (Supplementary Figs. 4 and 5), we focused on path-based reasoning because our human evaluation study demonstrated that this design choice enhanced clinician comprehension and satisfaction25.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [25]
The findings from the pilot study comparison motivated the adoption of path-based explanations, which were preferred based on user feedback.
“The findings from this comparison motivated the adoption of path-based explanations, which were preferred based on user feedback.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [25]
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- Authors
- Wang, Q., Huang, K., Chandak, P., Zitnik, M., & Gehlenborg, N.
- Affiliations
- Harvard University (US)
- Stanford University (US)
- Massachusetts Institute of Technology (US)
- Harvard University, USA
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2023-01
- Volume
- 29
- Issue
- 1
- Pages
- 1266-1276
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W4304757966
- Cited by
- 82
- References
- 75
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 29 · 2023: 11 · 2022: 1
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- hybrid
- doi.org
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- Funders
- NSF (IIS-2030459, IIS-2033384)
- Air Force Contract (FA8702-15-D-0001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- explainable AI, graph neural networks, drug repurposing, visualization design, meta-paths, domain experts, scientific visualization
- MeSH terms
- Algorithms, Computer Graphics, Neural Networks, Computer, Drug Repositioning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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