B3Clustering: Identifying protein complexes from protein-protein interaction network

Chin, Eunjung and Zhu, Jia (2013). B3Clustering: Identifying protein complexes from protein-protein interaction network. In: Yoshiharu Ishikawa, Jianzhong Li, Wei Wang, Rui Zhang and Wenjie Zhang, Web Technologies and Applications - 15th Asia-Pacific Web Conference, APWeb 2013, Proceedings. 15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013, Sydney, NSW Australia, (108-119). 4 - 6 April 2013. doi:10.1007/978-3-642-37401-2_13


Author Chin, Eunjung
Zhu, Jia
Title of paper B3Clustering: Identifying protein complexes from protein-protein interaction network
Conference name 15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013
Conference location Sydney, NSW Australia
Conference dates 4 - 6 April 2013
Proceedings title Web Technologies and Applications - 15th Asia-Pacific Web Conference, APWeb 2013, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1007/978-3-642-37401-2_13
Open Access Status DOI
ISBN 9783642374005
9783642374012
ISSN 0302-9743
1611-3349
Editor Yoshiharu Ishikawa
Jianzhong Li
Wei Wang
Rui Zhang
Wenjie Zhang
Volume 7808 LNCS
Start page 108
End page 119
Total pages 12
Language eng
Abstract/Summary Cluster analysis is one of most important challenges for data mining in the modern Biology. The advance of experimental technologies have produced large amount of binary protein-protein interaction data, but it is hard to find protein complexes in vitro.We introduce new algorithm called B3Clustering which detects densely connected subgraphs from the complicated and noisy graph. B3Clustering finds clusters by adjusting the density of subgraphs to be flexible according to its size, because the more vertices the cluster has, the less dense it becomes. B3Clustering bisects the paths with distance of 3 into two groups to select vertices from each group.We experiment B3Clustering and two other clustering methods in three different PPI networks. Then, we compare the resultant clusters from each method with benchmark complexes called CYC2008. The experimental result supports the efficiency and robustness of B3Clustering for protein complex prediction in PPI networks.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
 
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