Semi-bipartite graph visualization for gene ontology networks

Xu, Kai and Williams, Rohan and Hong, Seok-Hee and Liu, Qing and Zhang, Ji ORCID: https://orcid.org/0000-0001-7167-6970 (2010) Semi-bipartite graph visualization for gene ontology networks. In: GD 2009: 17th International Symposium on Graph Drawing, 22-25 Sep 2009, Chicago, United States.

Preview	Text (Author's Version) Xu_Williams_Hong_Liu_Zhang_AV.pdf Download (291kB)
Preview	Text (Published Version) Xu_Williams_Hong_Liu_Zhang_GD2009_PV.pdf Download (220kB)
Preview	Text (Documentation) Binder1.pdf Download (778kB)

Abstract

In this paper we propose three layout algorithms for semi-bipartite graphs-bipartite graphs with edges in one partition-that emerge from microarray experiment analysis. We also introduce a method that effectively reduces visual complexity by removing less informative nodes. The drawing quality and running time are evaluated with five real-world datasets, and the results show significant reduction in crossing number and total edge length. All the proposed methods are available in visualization package GEOMI, and are well received by domain users.

Statistics for this ePrint Item

Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	Author's version deposited in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com)
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited:	20 Oct 2009 23:49
Last Modified:	22 Jun 2018 01:22
Uncontrolled Keywords:	semi-bipartite graphs; bipartite graphs; gene ontology networks
Fields of Research (2008):	08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining 11 Medical and Health Sciences > 1112 Oncology and Carcinogenesis > 111203 Cancer Genetics 06 Biological Sciences > 0604 Genetics > 060405 Gene Expression (incl. Microarray and other genome-wide approaches)
Fields of Research (2020):	46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified 32 BIOMEDICAL AND CLINICAL SCIENCES > 3211 Oncology and carcinogenesis > 321103 Cancer genetics 31 BIOLOGICAL SCIENCES > 3105 Genetics > 310505 Gene expression (incl. microarray and other genome-wide approaches)
Socio-Economic Objectives (2008):	E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI:	https://doi.org/10.1007/978-3-642-11805-0_24
URI:	http://eprints.usq.edu.au/id/eprint/5802

Actions (login required)

Archive Repository Staff Only