Literature DB >> 11284008

Assessment of prediction accuracy of protein function from protein--protein interaction data.

H Hishigaki1, K Nakai, T Ono, A Tanigami, T Takagi.   

Abstract

Functional prediction of open reading frames coded in the genome is one of the most important tasks in yeast genomics. Among a number of large-scale experiments for assigning certain functional classes to proteins, experiments determining protein-protein interaction are especially important because interacting proteins usually have the same function. Thus, it seems possible to predict the function of a protein when the function of its interacting partner is known. However, in vitro experiments often suffer from artifacts and a protein can often have multiple binding partners with different functions. We developed an objective prediction method that can systematically include the information of indirect interaction. Our method can predict the subcellular localization, the cellular role and the biochemical function of yeast proteins with accuracies of 72.7%, 63.6% and 52.7%, respectively. The prediction accuracy rises for proteins with more than three binding partners and thus we present the open prediction results for 16 such proteins. Copyright 2001 John Wiley & Sons, Ltd.

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Year:  2001        PMID: 11284008     DOI: 10.1002/yea.706

Source DB:  PubMed          Journal:  Yeast        ISSN: 0749-503X            Impact factor:   3.239


  86 in total

1.  Relating whole-genome expression data with protein-protein interactions.

Authors:  Ronald Jansen; Dov Greenbaum; Mark Gerstein
Journal:  Genome Res       Date:  2002-01       Impact factor: 9.043

2.  Interaction generality, a measurement to assess the reliability of a protein-protein interaction.

Authors:  Rintaro Saito; Harukazu Suzuki; Yoshihide Hayashizaki
Journal:  Nucleic Acids Res       Date:  2002-03-01       Impact factor: 16.971

3.  Topological structure analysis of the protein-protein interaction network in budding yeast.

Authors:  Dongbo Bu; Yi Zhao; Lun Cai; Hong Xue; Xiaopeng Zhu; Hongchao Lu; Jingfen Zhang; Shiwei Sun; Lunjiang Ling; Nan Zhang; Guojie Li; Runsheng Chen
Journal:  Nucleic Acids Res       Date:  2003-05-01       Impact factor: 16.971

4.  Computational approaches to protein-protein interaction.

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

5.  Whole-genome annotation by using evidence integration in functional-linkage networks.

Authors:  Ulas Karaoz; T M Murali; Stan Letovsky; Yu Zheng; Chunming Ding; Charles R Cantor; Simon Kasif
Journal:  Proc Natl Acad Sci U S A       Date:  2004-02-23       Impact factor: 11.205

Review 6.  Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system.

Authors:  Bram Stynen; Hélène Tournu; Jan Tavernier; Patrick Van Dijck
Journal:  Microbiol Mol Biol Rev       Date:  2012-06       Impact factor: 11.056

7.  Assessing and combining reliability of protein interaction sources.

Authors:  Sonia Leach; Aaron Gabow; Lawrence Hunter; Debra S Goldberg
Journal:  Pac Symp Biocomput       Date:  2007

Review 8.  Network integration and graph analysis in mammalian molecular systems biology.

Authors:  A Ma'ayan
Journal:  IET Syst Biol       Date:  2008-09       Impact factor: 1.615

9.  Integrative approaches for predicting protein function and prioritizing genes for complex phenotypes using protein interaction networks.

Authors:  Xiaotu Ma; Ting Chen; Fengzhu Sun
Journal:  Brief Bioinform       Date:  2013-06-19       Impact factor: 11.622

10.  The role of indirect connections in gene networks in predicting function.

Authors:  Jesse Gillis; Paul Pavlidis
Journal:  Bioinformatics       Date:  2011-05-06       Impact factor: 6.937

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