A supervised approach to detect protein complex by combining biological and topological properties

Authors:
Yang Yu;Xiaolong Wang;Lei Lin;Chengjie Sun;Xuan Wang
Affiliations:
Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001 China/ Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate Sch ...;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001 China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

Citing 12
Cited 0

Functional topology in a network of protein interactions

Bioinformatics
Protein complex prediction via cost-based clustering

Bioinformatics
GO: :TermFinder---open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

Bioinformatics
Mining coherent dense subgraphs across massive biological networks for functional discovery

Bioinformatics
Tools for large graph mining

Tools for large graph mining
CFinder: locating cliques and overlapping modules in biological networks

Bioinformatics
Mining and analysing scale-free protein protein interaction network

International Journal of Bioinformatics Research and Applications
Protein complex identification by supervised graph local clustering

Bioinformatics
Identifying the overlapping complexes in protein interaction networks

International Journal of Data Mining and Bioinformatics
Prediction of protein protein interactions from primary sequences

International Journal of Data Mining and Bioinformatics
A hybrid clustering algorithm for identifying modules in Protein Protein Interaction networks

International Journal of Data Mining and Bioinformatics
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Protein biochemical functions are revealed in the form of protein complexes from the Protein-Protein Interaction PPI network. In this paper, we propose a supervised based algorithm by combining biological and topological properties to detect protein complexes in PPI networks, in which protein amino acid background frequency is introduced as biological properties and become a new block of the features. In comparison with other established methods, the evaluation results indicate that the applied method can achieve comparable performances and match more meaningful real complexes. We also demonstrate that the use of protein amino acid background frequency and the SVM based method can efficiently improve the performance.