Literature DB >> 21233530

Visual exploration across biomedical databases.

Michael D Lieberman1, Sima Taheri, Huimin Guo, Fatemeh Mirrashed, Inbal Yahav, Aleks Aris, Ben Shneiderman.   

Abstract

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.

Mesh:

Year:  2011        PMID: 21233530     DOI: 10.1109/TCBB.2010.1

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  1 in total

1.  ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery.

Authors:  Christian Partl; Alexander Lex; Marc Streit; Hendrik Strobelt; Anne-Mai Wassermann; Hanspeter Pfister; Dieter Schmalstieg
Journal:  IEEE Trans Vis Comput Graph       Date:  2014-12       Impact factor: 4.579

  1 in total

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