Academic Journal
Geranium: Multimodal Retrieval of Genomics Data Visualizations.
| Title: | Geranium: Multimodal Retrieval of Genomics Data Visualizations. |
|---|---|
| Authors: | Nguyen HN, L'Yi S, Smits TC, Gao S, Zitnik M, Gehlenborg N |
| Source: | IEEE transactions on visualization and computer graphics [IEEE Trans Vis Comput Graph] 2026 Jul; Vol. 32 (7), pp. 6447-6463. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9891704 Publication Model: Print Cited Medium: Internet ISSN: 1941-0506 (Electronic) Linking ISSN: 10772626 NLM ISO Abbreviation: IEEE Trans Vis Comput Graph Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York, NY : IEEE Computer Society, c1995- |
| MeSH Terms: | Genomics*/methods , Information Storage and Retrieval*/methods , Computer Graphics* , Data Visualization*, User-Computer Interface ; Humans |
| Abstract: | Effective visualization is essential for interpreting genomics data, yet researchers often face challenges in finding relevant, reusable examples. Existing tools offer limited support for searching the vast landscape of genomics visualizations, making the process of authoring new visualizations time-consuming and inefficient. To address this gap, we introduce Geranium, a data visualization retrieval system for searching and authoring genomics visualizations. Geranium supports multimodal retrieval, enabling users to query with images, text, or grammar-based specifications. Retrieved examples serve as scaffolds for authoring, providing templates that researchers can adapt with their own data, thereby streamlining the mechanics of visualization construction. Geranium integrates three embedding methods to combine specialized and general knowledge: grammar-based embeddings tailored to genomics visualizations, multimodal embeddings from a biomedical vision-language foundation model, and text embeddings from a fine-tuned large language model. For each visualization, we construct a multimodal representation that includes a Gosling specification, a pixel-based rendering, and natural language descriptions. We evaluate embedding strategies to maximize top-$k$k retrieval accuracy and conduct user studies with domain collaborators to gather feedback on usability. Our collection comprises 3,200 visualizations across 50 categories, ranging from single-view to coordinated multi-view designs and supporting applications from single-cell epigenomics to structural variation analysis. |
| Grant Information: | K99 HG013348 United States HG NHGRI NIH HHS; R01 HG011773 United States HG NHGRI NIH HHS; UM1 HG011536 United States HG NHGRI NIH HHS |
| Entry Date(s): | Date Created: 20260413 Date Completed: 20260623 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13275142 |
| DOI: | 10.1109/TVCG.2026.3683429 |
| PMID: | 41973568 |
| Database: | MEDLINE |
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