Generating Abstract Networks Using Multi-relational Biological Data

Author

Caravelli, Paul ; Beard, Mitch ; Gopolan, Brian ; Singh, Lisa ; Hu, Zhang-Zhi

Author_Institution

Dept. of Comput. Sci., Georgetown Univ., Washington, DC, USA

fYear

2009

fDate

15-17 July 2009

Firstpage

331

Lastpage

336

Abstract

This paper presents an approach for visual exploration of groups in network data. We let users visually cluster nodes based on common semantic and relational features. We describe the clusters in the context of multi-relational protein data. Finally, we illustrate the clusters as composite nodes using a visual analytic tool and show how to create a meaningful abstracted protein network by connecting these composite nodes based on common membership or common attribute features.

Keywords

biology computing; data visualisation; pattern clustering; proteins; abstract networks; abstracted protein network; cluster nodes; common attribute features; common membership; composite nodes; multirelational biological data; multirelational protein data; visual analytic tool; visual exploration; Biological system modeling; Biology computing; Computer networks; DC generators; Data mining; Data visualization; Joining processes; Oncology; Proteins; Visual analytics; abstract networks; protein network; visual analytics;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Visualisation, 2009 13th International Conference

Conference_Location

Barcelona

ISSN

1550-6037

Print_ISBN

978-0-7695-3733-7

Type

conf

DOI

10.1109/IV.2009.73

Filename

5190851