Predicting Protein-Protein Interactions by Association Mining

被引:0
作者
机构
来源
Information Systems Frontiers | 2006年 / 8卷
关键词
Protein-protein interactions; Association mining;
D O I
暂无
中图分类号
学科分类号
摘要
Identifying protein-protein interactions is a key problem in molecular biology. Currently, interactions cannot be reliably predicted on a proteome-wide scale but direct and indirect evidence for interactions is increasingly available from high-throughput interaction detection methods, gene expression microarrays, and protein annotation projects. In this paper we propose an association mining approach to integrating these diverse types of evidence. We apply this approach to a number of datasets consisting of interacting and non-interacting protein pairs annotated with different types of evidence. We identify patterns that distinguish interacting and non-interacting protein pairs, and use these patterns to assign a confidence level to proposed interactions.
引用
收藏
页码:37 / 47
页数:10
相关论文
共 164 条
[11]  
Deane CM(2001)Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry Science 294 2364-2368
[12]  
Salwinski L(2002)Systematic genetic analysis with ordered arrays of yeast deletion mutants Current Opinion in Structural Biology 12 368-373
[13]  
Xenarios I(2003)Computational methods for the prediction of protein interactions J. Mol. Biol. 327 919-923
[14]  
Eisenberg D(2003)How reliable are experimental protein-protein interaction data? Nucleic Acids Res. 31 251-254
[15]  
von Mering C(2003)Interdom: A database of putative interacting protein domains for validating predicted protein interactions and complexes Proc Natl Acad Sci USA 100 4372-4376
[16]  
Krause R(2003)Assessing experimentally derived interactions in a small world Science 302 449-453
[17]  
Snel B(2004)A Bayesian networks approach for predicting protein-protein interactions from genomic data BMC Bioinformatics 5 38-33
[18]  
Cornell M(2001)Predicting co-complexed protein pairs using genomic and proteomic data integration Med. Inform. Internet. Med. 26 25-1403
[19]  
Oliver SG(2001)Discovery of association rules in medical data Medinfo 10 1399-86
[20]  
Fields S(2003)Mining association rules from clinical databases: An intelligent diagnostic process in healthcare Bioinformatics 19 79-714