Journal article
A novel proteins complex identification based on connected affinity and multi-level seed extension
International journal of data mining and bioinformatics, v 14(1), pp 51-70
01 Jan 2016
Abstract
The identification of modules in complex networks is important for the understanding of biological systems. Recent studies have shown those modules can be identified from the protein interaction network, what's more, the modules has not only relatively high density, but also has high coefficient of affinity. In this paper, we propose a novel algorithm based on Connected Affinity and Multi-level Seed Extension (CAMSE). First, CAMSE integrates Protein Interactions (PPI) with the protein Connected Coefficient (CC) inferred from protein complexes collected in the MIPS database to enhance the modularisation and biological character. Then we complete the seed selection, inner kernel extensions and outer extension to get core candidate function modules step by step. Finally, we integrated the modules with high repeat rate. The experimental results show that CAMSE can detect the functional modules much more effectively and accurately when it compared with other state-of-art algorithms CPM, CACE and IPC-MCE.
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Details
- Title
- A novel proteins complex identification based on connected affinity and multi-level seed extension
- Creators
- Tingting He - Central China Normal UniversityPeng Li - Central China Normal UniversityXiaohua Hu - Central China Normal UniversityXianjun Shen - Central China Normal UniversityYan Wang - Central China Normal UniversityJunmin Zhao - Central China Normal University
- Publication Details
- International journal of data mining and bioinformatics, v 14(1), pp 51-70
- Publisher
- Inderscience Enterprises Ltd
- Number of pages
- 20
- Grant note
- 2014BHE0017 / international cooperation project of Hubei Province CCNU15ZD003; CCNU14A02008 / self-determined research Funds of CCNU from the colleges' basic research and operation of MOE ZDI125-1; WT125-30; WT125-44 / Project of State Language Commission 12; 2D223 / Major Project of National Social Science Fund
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000366136100004
- Scopus ID
- 2-s2.0-84948800088
- Other Identifier
- 991019167342004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology