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.
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.
Authors: Jean-François Rual; Kavitha Venkatesan; Tong Hao; Tomoko Hirozane-Kishikawa; Amélie Dricot; Ning Li; Gabriel F Berriz; Francis D Gibbons; Matija Dreze; Nono Ayivi-Guedehoussou; Niels Klitgord; Christophe Simon; Mike Boxem; Stuart Milstein; Jennifer Rosenberg; Debra S Goldberg; Lan V Zhang; Sharyl L Wong; Giovanni Franklin; Siming Li; Joanna S Albala; Janghoo Lim; Carlene Fraughton; Estelle Llamosas; Sebiha Cevik; Camille Bex; Philippe Lamesch; Robert S Sikorski; Jean Vandenhaute; Huda Y Zoghbi; Alex Smolyar; Stephanie Bosak; Reynaldo Sequerra; Lynn Doucette-Stamm; Michael E Cusick; David E Hill; Frederick P Roth; Marc Vidal Journal: Nature Date: 2005-09-28 Impact factor: 49.962
Authors: Nevan J Krogan; Gerard Cagney; Haiyuan Yu; Gouqing Zhong; Xinghua Guo; Alexandr Ignatchenko; Joyce Li; Shuye Pu; Nira Datta; Aaron P Tikuisis; Thanuja Punna; José M Peregrín-Alvarez; Michael Shales; Xin Zhang; Michael Davey; Mark D Robinson; Alberto Paccanaro; James E Bray; Anthony Sheung; Bryan Beattie; Dawn P Richards; Veronica Canadien; Atanas Lalev; Frank Mena; Peter Wong; Andrei Starostine; Myra M Canete; James Vlasblom; Samuel Wu; Chris Orsi; Sean R Collins; Shamanta Chandran; Robin Haw; Jennifer J Rilstone; Kiran Gandi; Natalie J Thompson; Gabe Musso; Peter St Onge; Shaun Ghanny; Mandy H Y Lam; Gareth Butland; Amin M Altaf-Ul; Shigehiko Kanaya; Ali Shilatifard; Erin O'Shea; Jonathan S Weissman; C James Ingles; Timothy R Hughes; John Parkinson; Mark Gerstein; Shoshana J Wodak; Andrew Emili; Jack F Greenblatt Journal: Nature Date: 2006-03-22 Impact factor: 49.962
Authors: Zhi-Ping Liu; Jiguang Wang; Yu-Qing Qiu; Ross K K Leung; Xiang-Sun Zhang; Stephen K W Tsui; Luonan Chen Journal: BMC Bioinformatics Date: 2012-05-08 Impact factor: 3.169