Mining maximal frequent dense subgraphs without candidate maintenance in PPI networks

Miao Wang, Xuequn Shang, Xiaogang Lei, Zhanhuai Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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 high-throughput 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. It adopts several techniques to achieve efficient mining. We evaluate our approach on four human PPI data sets. The experimental results show our approach has good performance in terms of efficiency.

Original languageEnglish
Title of host publicationProceedings - 2010 2nd International Workshop on Intelligent Systems and Applications, ISA 2010
DOIs
StatePublished - 2010
Event2nd International Workshop on Intelligent Systems and Applications, ISA2010 - Wuhan, China
Duration: 22 May 201023 May 2010

Publication series

NameProceedings - 2010 2nd International Workshop on Intelligent Systems and Applications, ISA 2010

Conference

Conference2nd International Workshop on Intelligent Systems and Applications, ISA2010
Country/TerritoryChina
CityWuhan
Period22/05/1023/05/10

Keywords

  • Family subgraph
  • Frequent dense subgraph
  • PPI
  • Used edge

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