Literature DB >> 16055319

Discovering reliable protein interactions from high-throughput experimental data using network topology.

Jin Chen1, Wynne Hsu, Mong Li Lee, See-Kiong Ng.   

Abstract

OBJECTIVE: Current protein-protein interaction (PPI) detection via high-throughput experimental methods, such as yeast-two-hybrid has been reported to be highly erroneous, leading to potentially costly spurious discoveries. This work introduces a novel measure called IRAP, i.e. "interaction reliability by alternative path", for assessing the reliability of protein interactions based on the underlying topology of the PPI network. METHODS AND MATERIALS: A candidate PPI is considered to be reliable if it is involved in a closed loop in which the alternative path of interactions between the two interacting proteins is strong. We devise an algorithm called AlternativePathFinder to compute the IRAP value for each interaction in a complex PPI network. Validation of the IRAP as a measure for assessing the reliability of PPIs is performed with extensive experiments on yeast PPI data. All the data used in our experiments can be downloaded from our supplementary data web site at .
RESULTS: Results show consistently that IRAP measure is an effective way for discovering reliable PPIs in large datasets of error-prone experimentally-derived PPIs. Results also indicate that IRAP is better than IG2, and markedly better than the more simplistic IG1 measure.
CONCLUSION: Experimental results demonstrate that a global, system-wide approach-such as IRAP that considers the entire interaction network instead of merely local neighbors-is a much more promising approach for assessing the reliability of PPIs.

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Year:  2005        PMID: 16055319     DOI: 10.1016/j.artmed.2005.02.004

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  12 in total

1.  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

2.  Clustering of High Throughput Gene Expression Data.

Authors:  Harun Pirim; Burak Ekşioğlu; Andy Perkins; Cetin Yüceer
Journal:  Comput Oper Res       Date:  2012-12       Impact factor: 4.008

3.  Protein complex prediction based on k-connected subgraphs in protein interaction network.

Authors:  Mahnaz Habibi; Changiz Eslahchi; Limsoon Wong
Journal:  BMC Syst Biol       Date:  2010-09-16

4.  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

5.  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

6.  Cluster-based assessment of protein-protein interaction confidence.

Authors:  Atanas Kamburov; Arndt Grossmann; Ralf Herwig; Ulrich Stelzl
Journal:  BMC Bioinformatics       Date:  2012-10-10       Impact factor: 3.169

Review 7.  Parameter estimate of signal transduction pathways.

Authors:  Ivan Arisi; Antonino Cattaneo; Vittorio Rosato
Journal:  BMC Neurosci       Date:  2006-10-30       Impact factor: 3.288

8.  Identifying protein complexes with fuzzy machine learning model.

Authors:  Bo Xu; Hongfei Lin; Kavishwar B Wagholikar; Zhihao Yang; Hongfang Liu
Journal:  Proteome Sci       Date:  2013-11-07       Impact factor: 2.480

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.  Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.

Authors:  Carlo Vittorio Cannistraci; Gregorio Alanis-Lobato; Timothy Ravasi
Journal:  Bioinformatics       Date:  2013-07-01       Impact factor: 6.937

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