Mining maximal frequent dense subgraphs without candidate maintenance in PPI networks

Miao Wang, Xuequn Shang, Xiaogang Lei, Zhanhuai Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Proceedings - 2010 2nd International Workshop on Intelligent Systems and Applications, ISA 2010
DOI
出版状态已出版 - 2010
活动2nd International Workshop on Intelligent Systems and Applications, ISA2010 - Wuhan, 中国
期限: 22 5月 201023 5月 2010

出版系列

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

会议

会议2nd International Workshop on Intelligent Systems and Applications, ISA2010
国家/地区中国
Wuhan
时期22/05/1023/05/10

指纹

探究 'Mining maximal frequent dense subgraphs without candidate maintenance in PPI networks' 的科研主题。它们共同构成独一无二的指纹。

引用此