Predicting interactions in protein networks by completing defective cliques

Authors:
Haiyuan Yu;Alberto Paccanaro;Valery Trifonov;Mark Gerstein
Affiliations:
Department of Molecular Biophysics and Biochemistry 266 Whitney Avenue Yale University PO Box 208114;Department of Molecular Biophysics and Biochemistry 266 Whitney Avenue Yale University PO Box 208114;Department of Computer Science, Yale University New Haven, CT 06520-8285, USA;Department of Computer Science, Yale University New Haven, CT 06520-8285, USA
Venue:
Bioinformatics
Year:
2006

Citing 0
Cited 12

Integration of topological measures for eliminating non-specific interactions in protein interaction networks

Discrete Applied Mathematics
Fixed-Parameter Algorithms for Graph-Modeled Date Clustering

TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
Finding Biologically Accurate Clusterings in Hierarchical Tree Decompositions Using the Variation of Information

RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Editing Graphs into Disjoint Unions of Dense Clusters

ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
A knowledge based decision support system for bioinformatics and system biology

CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Dense subgraphs with restrictions and applications to gene annotation graphs

RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
NetPipe: a network-based pipeline for discovery of genes and protein complexes regulating meiotic recombination hotspots

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Mining from protein–protein interactions

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Algorithms for detecting optimal hereditary structures in graphs, with application to clique relaxations

Computational Optimization and Applications
Improving protein complex classification accuracy using amino acid composition profile

Computers in Biology and Medicine
Identification of DNA-Binding and Protein-Binding Proteins Using Enhanced Graph Wavelet Features

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Approximating 2-cliques in unit disk graphs

Discrete Applied Mathematics

Quantified Score

Hi-index	3.84

Visualization

Abstract

Datasets obtained by large-scale, high-throughput methods for detecting protein--protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein--protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/ Contact: Mark.Gerstein@yale.edu Supplementary information: Supplementary Materials are available at Bioinformatics online.