Literature DB >> 12691988

Construction of reliable protein-protein interaction networks with a new interaction generality measure.

Rintaro Saito1, Harukazu Suzuki, Yoshihide Hayashizaki.   

Abstract

MOTIVATION: Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed.
RESULTS: We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.

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Year:  2003        PMID: 12691988     DOI: 10.1093/bioinformatics/btg070

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  24 in total

1.  Computational approaches to protein-protein interaction.

Authors:  Giacomo Franzot; Oliviero Carugo
Journal:  J Struct Funct Genomics       Date:  2003

2.  Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data.

Authors:  Zhu-Hong You; Ying-Ke Lei; Jie Gui; De-Shuang Huang; Xiaobo Zhou
Journal:  Bioinformatics       Date:  2010-09-03       Impact factor: 6.937

3.  Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

Authors:  Manuel Middendorf; Etay Ziv; Chris H Wiggins
Journal:  Proc Natl Acad Sci U S A       Date:  2005-02-22       Impact factor: 11.205

Review 4.  Embryonic stem cell interactomics: the beginning of a long road to biological function.

Authors:  Maram Yousefi; Vahid Hajihoseini; Woojin Jung; Batol Hosseinpour; Hassan Rassouli; Bonghee Lee; Hossein Baharvand; KiYoung Lee; Ghasem Hosseini Salekdeh
Journal:  Stem Cell Rev Rep       Date:  2012-12       Impact factor: 5.739

5.  Predicting direct protein interactions from affinity purification mass spectrometry data.

Authors:  Ethan Dh Kim; Ashish Sabharwal; Adrian R Vetta; Mathieu Blanchette
Journal:  Algorithms Mol Biol       Date:  2010-10-29       Impact factor: 1.405

6.  Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes.

Authors:  Carlo Vittorio Cannistraci
Journal:  Sci Rep       Date:  2018-10-25       Impact factor: 4.379

7.  Assessing and predicting protein interactions by combining manifold embedding with multiple information integration.

Authors:  Ying-Ke Lei; Zhu-Hong You; Zhen Ji; Lin Zhu; De-Shuang Huang
Journal:  BMC Bioinformatics       Date:  2012-05-08       Impact factor: 3.169

8.  Biomedical discovery acceleration, with applications to craniofacial development.

Authors:  Sonia M Leach; Hannah Tipney; Weiguo Feng; William A Baumgartner; Priyanka Kasliwal; Ronald P Schuyler; Trevor Williams; Richard A Spritz; Lawrence Hunter
Journal:  PLoS Comput Biol       Date:  2009-03-27       Impact factor: 4.475

9.  Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis.

Authors:  Christian Frech; Michael Kommenda; Viktoria Dorfer; Thomas Kern; Helmut Hintner; Johann W Bauer; Kamil Onder
Journal:  BMC Bioinformatics       Date:  2009-01-19       Impact factor: 3.169

10.  A core-attachment based method to detect protein complexes in PPI networks.

Authors:  Min Wu; Xiaoli Li; Chee-Keong Kwoh; See-Kiong Ng
Journal:  BMC Bioinformatics       Date:  2009-06-02       Impact factor: 3.169

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