Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data
Open Access
- 3 September 2010
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 26 (21), 2744-2751
- https://doi.org/10.1093/bioinformatics/btq510
Abstract
Motivation: High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries, and thus high-quality protein–protein interaction (PPI) maps are critical for a deeper understanding of cellular processes. However, the unreliability and paucity of current available PPI data are key obstacles to the subsequent quantitative studies. It is therefore highly desirable to develop an approach to deal with these issues from the computational perspective. Most previous works for assessing and predicting protein interactions either need supporting evidences from multiple information resources or are severely impacted by the sparseness of PPI networks. Results: We developed a robust manifold embedding technique for assessing the reliability of interactions and predicting new interactions, which purely utilizes the topological information of PPI networks and can work on a sparse input protein interactome without requiring additional information types. After transforming a given PPI network into a low-dimensional metric space using manifold embedding based on isometric feature mapping (ISOMAP), the problem of assessing and predicting protein interactions is recasted into the form of measuring similarity between points of its metric space. Then a reliability index, 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 embedded space. Validation of the proposed method is performed with extensive experiments on densely connected and sparse PPI network of yeast, respectively. Results demonstrate that the interactions ranked top by our method have high-functional homogeneity and localization coherence, especially our method is very efficient for large sparse PPI network with which the traditional algorithms fail. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks. Availability: MATLAB code implementing the algorithm is available from the web site http://home.ustc.edu.cn/∼yzh33108/Manifold.htm. Contact:dshuang@iim.ac.cn Supplementary information:Supplementary data are available at Bioinformatics online.Keywords
This publication has 37 references indexed in Scilit:
- Fitting a geometric graph to a protein–protein interaction networkBioinformatics, 2008
- A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentialityBMC Bioinformatics, 2007
- Biological network comparison using graphlet degree distributionBioinformatics, 2007
- Network‐based prediction of protein functionMolecular Systems Biology, 2007
- Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactionsBioinformatics, 2006
- Global landscape of protein complexes in the yeast Saccharomyces cerevisiaeNature, 2006
- Proteome survey reveals modularity of the yeast cell machineryNature, 2006
- Towards a proteome-scale map of the human protein–protein interaction networkNature, 2005
- Conserved network motifs allow protein–protein interaction predictionBioinformatics, 2004
- Prediction of Protein Function Using Protein–Protein Interaction DataJournal of Computational Biology, 2003