Paper Info
Title
Increasing the reliability of protein-protein interaction networks via non-convex semantic embedding
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.
Year
DOI
Venue
2013
10.1016/j.neucom.2013.04.027
Neurocomputing
Keywords
Field
DocType
NONLINEAR DIMENSIONALITY REDUCTION,THROUGHPUT EXPERIMENTAL-DATA,SACCHAROMYCES-CEREVISIAE,INTERACTION MAP,COMPLEXES,PREDICTION,GENERALITY,EIGENMAPS
Noise reduction,MATLAB,Embedding,Source code,Regular polygon,Robustness (computer science),Artificial intelligence,Distance matrix,Euclidean geometry,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
121,
0925-2312
11
PageRank 
References 
Authors
0.50
26
3
Name
Order
Citations
PageRank
Lin Zhu1744.93
Zhuhong You274855.20
De-Shuang Huang35532357.50