Increasing the reliability of protein-protein interaction networks via non-convex semantic embedding

Authors:
Lin Zhu;Zhu-Hong You;De-Shuang Huang
Affiliations:
-;-;-
Venue:
Neurocomputing
Year:
2013

Citing 19
Cited 0

LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares

ACM Transactions on Mathematical Software (TOMS)
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Conserved network motifs allow protein--protein interaction prediction

Bioinformatics
Modeling interactome: scale-free or geometric?

Bioinformatics
Effective Pre-Processing Strategies for Functional Clustering of a Protein-Protein Interactions Network

BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Robust Euclidean embedding

ICML '06 Proceedings of the 23rd international conference on Machine learning
Exploiting indirect neighbours and topological weight to predict protein function from protein--protein interactions

Bioinformatics
A lock-and-key model for protein--protein interactions

Bioinformatics
Biological network comparison using graphlet degree distribution

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

Bioinformatics
Riemannian Manifold Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Training robust support vector machine with smooth Ramp loss in the primal space

Neurocomputing
Discovering reliable protein interactions from high-throughput experimental data using network topology

Artificial Intelligence in Medicine
Random classification noise defeats all convex potential boosters

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

Bioinformatics
Unsupervised learning of image manifolds by semidefinite programming

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nonconvex Online Support Vector Machines

IEEE Transactions on Pattern Analysis and Machine Intelligence
Composite Binary Losses

The Journal of Machine Learning Research
Inferring Networks of Diffusion and Influence

ACM Transactions on Knowledge Discovery from Data (TKDD)

Quantified Score

Hi-index	0.01

Visualization

Abstract

Over the last decade, the development of high-throughput techniques has resulted in a rapid accumulation of protein-protein interaction (PPI) data. However, the high-throughput experimental interaction data is prone to exhibit high level of noise. Despite the promising performance of current geometric approaches for increasing the reliability of PPI networks, it is still of major concern to find a better method that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called non-convex semantic embedding (NCSE) for assessing the reliability of interactions. Unlike previous approaches which seek to preserve a predefined distance matrix in the embedding space, NCSE tries to adaptively learn a Euclidean embedding under the simple geometric assumption of PPI networks. We also propose using non-convex cost function in order to improve the robustness of NCSE. The experimental results show that our approach substantially outperforms previous methods on PPI assessment problems. NCSE could thus facilitate further graph-based studies of PPIs and may help infer their hidden underlying biological knowledge. The Matlab source code of NCSE is freely available upon request.