Visualization of protein-protein interaction network for knowledge discovery

Author

Zhu, Weizhong ; Lin, Xia ; Hu, Xiaohua ; Sokhansanj, Bahrad A.

Author_Institution

Coll. of Inf. Sci. & Technol., Drexel Univ., Philadelphia, PA, USA

Volume

1

fYear

2005

fDate

25-27 July 2005

Firstpage

373

Abstract

A new visualization tool, called "Visual Concept Explorer (VCE)", was developed to visualize concept relationships in bio-medical literature. VCE integrates pathfinder network scaling and Kohonen self-organizing feature map algorithm for visual mapping. As a case study, VCE was applied to visualize a chromatin protein-protein interaction (PPI) network. The mapping results demonstrated that VCE could explore the semantic structure and latent domain knowledge hidden in protein-protein interaction data sets generated from bio-medical literature.

Keywords

biology computing; data mining; data visualisation; proteins; self-organising feature maps; semantic networks; Kohonen self-organizing feature map algorithm; Visual Concept Explorer; biomedical literature; chromatin protein-protein interaction network visualization; knowledge discovery; pathfinder network scaling; semantic structure; visual mapping; Clustering algorithms; Data mining; Data visualization; Engineering profession; Frequency; Fungi; Information science; Neurons; Proteins; Proteomics; information visualization; knowledge discovery; protein-protein interaction network;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing, 2005 IEEE International Conference on

Print_ISBN

0-7803-9017-2

Type

conf

DOI

10.1109/GRC.2005.1547307

Filename

1547307