@inproceedings{f8ab9792317c48699e80f1f85fa00ae4,
title = "Mining maximal frequent dense subgraphs without candidate maintenance in PPI networks",
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.",
keywords = "Family subgraph, Frequent dense subgraph, PPI, Used edge",
author = "Miao Wang and Xuequn Shang and Xiaogang Lei and Zhanhuai Li",
year = "2010",
doi = "10.1109/IWISA.2010.5473237",
language = "英语",
isbn = "9781424458745",
series = "Proceedings - 2010 2nd International Workshop on Intelligent Systems and Applications, ISA 2010",
booktitle = "Proceedings - 2010 2nd International Workshop on Intelligent Systems and Applications, ISA 2010",
note = "2nd International Workshop on Intelligent Systems and Applications, ISA2010 ; Conference date: 22-05-2010 Through 23-05-2010",
}