ProRank: a method for detecting protein complexes

Authors:
Nazar Zaki;Jose Berengueres;Dmitry Efimov
Affiliations:
United Arab Emirates University, Al Ain, Uae;United Arab Emirates University, Al Ain, Uae;Moscow State University, Moscow, Russian Fed
Venue:
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Year:
2012

Citing 12
Cited 2

Matrix analysis

Matrix analysis
The anatomy of a large-scale hypertextual Web search engine

WWW7 Proceedings of the seventh international conference on World Wide Web 7
Functional topology in a network of protein interactions

Bioinformatics
Protein homology detection using string alignment kernels

Bioinformatics
Protein complex prediction via cost-based clustering

Bioinformatics
The $25,000,000,000 Eigenvector: The Linear Algebra behind Google

SIAM Review
Motif-based protein ranking by network propagation

Bioinformatics
CFinder: locating cliques and overlapping modules in biological networks

Bioinformatics
Exploiting indirect neighbours and topological weight to predict protein function from protein--protein interactions

Bioinformatics
Complex discovery from weighted PPI networks

Bioinformatics
Identification of protein complexes from co-immunoprecipitation data

Bioinformatics
Construction of co-complex score matrix for protein complex prediction from AP-MS data

Bioinformatics

Detecting protein complexes from noisy protein interaction data

Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
Restricted neighborhood search clustering revisited: an evolutionary computation perspective

PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. Observations show that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. This paper introduces a novel method for detecting protein-complexes from PPI by using a protein ranking algorithm (ProRank) and incorporating an evolutionary relationships between proteins in the network. The method successfully predicted 57 out of 81 benchmarked protein complexes created from the Munich Information Center for Protein Sequence (MIPS). The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of our proposed method. Datasets, programs and results are available at http://faculty.uaeu.ac.ae/nzaki/ProRank.htm.