Volume 6 Number 3 (Mar. 2011)
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JSW 2011 Vol.6(3): 498-507 ISSN: 1796-217X
doi: 10.4304/jsw.6.3.498-507

MFC: Mining Maximal Frequent Dense Subgraphs without Candidate Maintenance in Imbalanced PPI Networks

Miao Wang, Xuequn Shang, Zhanhuai Li
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China

Abstract—The prediction of protein function is one of the most challenging problems in bioinformatics. Several studies have shown that the prediction using PPI is promising. However, the PPI data generated from highthroughput experiments are very noisy, which renders great challenges to the existing methods. In this paper, we propose an algorithm, MFC, to efficiently mine maximal frequent dense subgraphs without candidate maintenance in PPI networks. Instead of using summary graph, MFC produces frequent dense patterns by extending vertices. It adopts several techniques to achieve efficient mining. Due to the imbalance character of PPI network, we also propose to generate frequent patterns using relative support. We evaluate our approach on four PPI data sets. The experimental results show that our approach has good performance in terms of efficiency. With the help of relative support, more frequent dense functional interaction patterns in the PPI networks can be identified.

Index Terms—frequent dense subgraph, imbalance, relative support, used edge, family subgraph


Cite: Miao Wang, Xuequn Shang, Zhanhuai Li, "MFC: Mining Maximal Frequent Dense Subgraphs without Candidate Maintenance in Imbalanced PPI Networks," Journal of Software vol. 6, no. 3, pp. 498-507, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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