Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Context-Driven Narrative Visualizations for "Big Data" Applications in Medical Biology. |
| Συγγραφείς: |
Blenman KR, Qiu S, Rushmeier H |
| Πηγή: |
IEEE computer graphics and applications [IEEE Comput Graph Appl] 2026 Jul-Aug; Vol. 46 (4), pp. 127-134. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9881869 Publication Model: Print Cited Medium: Internet ISSN: 1558-1756 (Electronic) Linking ISSN: 02721716 NLM ISO Abbreviation: IEEE Comput Graph Appl Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: [Los Alamitos, CA] : IEEE Computer Society : National Computer Graphics Association, [c1981- |
| Ιατρικοί όροι (MeSH): |
Computational Biology*/methods , Computer Graphics* , Big Data*, Humans ; Proteomics ; User-Computer Interface ; Genomics |
| Περίληψη: |
Medical biology data have grown exponentially and are more complex than our minds can process. It is common for omics datasets obtained from measuring molecular entities (e.g., genes in genomics and proteins in proteomics) to undergo open-ended analysis due to the complexity of biological systems. Rather than probing for a single answer, researchers often explore the data to discover multiple new insights, form actionable next steps, and frame new questions. However, existing visualizations are limited in their ability to 1) help understand the details of the molecular entities that they contain; 2) provide clues of how to move forward; and 3) show which structural biological entities are relevant. We describe how context-driven narrative visualizations with guidance can address these limitations. We describe a prototype, visAPPprot, as an example. |
| Entry Date(s): |
Date Created: 20260728 Date Completed: 20260729 Latest Revision: 20260729 |
| Update Code: |
20260730 |
| DOI: |
10.1109/MCG.2026.3697761 |
| PMID: |
42519877 |
| Βάση Δεδομένων: |
MEDLINE |