Identifying Spurious Interactions and Predicting Missing Interactions in the Protein-Protein Interaction Networks via a Generative Network Model

Authors:
Yuan Zhu;Xiao-Fei Zhang;Dao-Qing Dai;Meng-Yun Wu
Affiliations:
Sun Yat-Sen Univisity, Guang Zhou -Guang Zhou;Sun Yat-Sen Univisity , -Guang Zhou Guangzhou;Sun Yat-Sen University, Guangzhou;Sun Yat-Sen Univisity, -Guang Zhou
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2013

Citing 11
Cited 0

Modeling interactome: scale-free or geometric?

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

Bioinformatics
Increasing confidence of protein interactomes using network topological metrics

Bioinformatics
Exploring biological network structure using exponential random graph models

Bioinformatics
Fitting a geometric graph to a protein–protein interaction network

Bioinformatics
Mixed Membership Stochastic Blockmodels

The Journal of Machine Learning Research
A Poisson model for random multigraphs

Bioinformatics
Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data

Bioinformatics
Bioinformatics
A Framework for Incorporating Functional Interrelationships into Protein Function Prediction Algorithms

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized Sparse Generative Network Model

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the rapid development of high-throughput experiment techniques for protein-protein interaction (PPI) detection, a large amount of PPI network data are becoming available. However, the data produced by these techniques have high levels of spurious and missing interactions. This study assigns a new reliably indication for each protein pairs via the new generative network model (RIGNM) where the scale-free property of the PPI network is considered to reliably identify both spurious and missing interactions in the observed high-throughput PPI network. The experimental results show that the RIGNM is more effective and interpretable than the compared methods, which demonstrate that this approach has the potential to better describe the PPI networks and drive new discoveries.