Visual exploration across biomedical databases

Michael D. Lieberman, Sima Taheri, Huimin Guo, Fatemeh Mirrashed, Inbal Yahav, Aleks Aris, Ben Shneiderman

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration.

Original languageEnglish
Article number5383347
Pages (from-to)536-550
Number of pages15
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number2
StatePublished - 2011
Externally publishedYes

Bibliographical note

Funding Information:
The authors wish to thank Sameer Antani, Olivier Bodenreider, Bruce Bray, Marcelo Fiszman, Dina Demner-Fushman, Michael Galperin, Adam Lee, Jimmy Lin, Adam Phillippy, Louiqa Raschid, Tom Rindflesch, Charles Sneiderman, George Thoma, and the anonymous reviewers for their invaluable comments and assistance in evaluating our methods. This work was supported in part by the US National Science Foundation (NSF) under grants EIA-00-91474, CCF-05-15241, and IIS-0713501, as well as the Office of Policy Development & Research of the Department of Housing and Urban Development, Microsoft Research, and NVIDIA.


  • Data exploration and discovery
  • bioinformatics
  • information visualization


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