Increasing reliability of protein interactome by fast manifold embedding

Authors:
Ying-Ke Lei;Zhu-Hong You;Tianbao Dong;Yun-Xiao Jiang;Jun-An Yang
Affiliations:
State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei 230027, Anhui, China and Anhui Key Laboratory of Electronic Restriction, Hefei 230037, China;College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, PR China;State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei 230027, Anhui, China;State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei 230027, Anhui, China;State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei 230027, Anhui, China
Venue:
Pattern Recognition Letters
Year:
2013

Citing 11
Cited 0

Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

SIAM Journal on Scientific Computing
Conserved network motifs allow protein--protein interaction prediction

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
Fitting a geometric graph to a protein–protein interaction network

Bioinformatics
Riemannian Manifold Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering reliable protein interactions from high-throughput experimental data using network topology

Artificial Intelligence in Medicine
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

Quantified Score

Hi-index	0.10

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 false-positive rates. It is therefore highly desirable to develop an approach to deal with these issues from the computational perspective. In this paper, we develop a robust computational technique for assessing the reliability of interactions by fast manifold embedding algorithm. A fast isometric feature mapping (fast-ISOMAP) is proposed to transform a PPI network into a low dimensional metric space, which recasts the problem of assessing protein interactions into the form of measuring similarity between points of its metric space. Then a reliability index (RI), a likelihood indicating the interaction of two proteins, is assigned to each protein pair in the PPI networks based on the similarity between the points in the embedding space. Validation of the proposed method is performed with extensive experiments on PPI networks of yeast. Results demonstrate that the interactions ranked top by our method have high functional homogeneity and localization coherence. Therefore, the proposed algorithm is a much more promising method to detect false positive interactions in PPI networks.