Nucleic Acids Research, 2002, Vol. 30, No. 1 303-305
© 2002 Oxford University Press
DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions
UCLA-DOE Laboratory of Structural Biology and Molecular Medicine, Molecular Biology Institute, PO Box 951570, UCLA, Los Angeles, CA 90095-1570, USA
The Database of Interacting Proteins (DIP: http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein–protein interactions. It provides the scientific community with an integrated set of tools for browsing and extracting information about protein interaction networks. As of September 2001, the DIP catalogs 
11 000 unique interactions among 5900 proteins from >80 organisms; the vast majority from yeast, Helicobacter pylori and human. Tools have been developed that allow users to analyze, visualize and integrate their own experimental data with the information about protein–protein interactions available in the DIP database.
* To whom correspondence should be addressed. Tel: +1 310 825 3754; Fax: +1 310 206 3914; Email: david{at}mbi.ucla.edu The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
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D. Cotter, P. Guda, E. Fahy, and S. Subramaniam MitoProteome: mitochondrial protein sequence database and annotation system Nucleic Acids Res., January 1, 2004; 32(90001): D463 - 467. [Abstract] [Full Text] [PDF]
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I. Stagljar Finding Partners: Emerging Protein Interaction Technologies Applied to Signaling Networks Sci. Signal., December 16, 2003; 2003(213): pe56 - pe56. [Abstract] [Full Text] [PDF]
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M. Strong, T. G. Graeber, M. Beeby, M. Pellegrini, M. J. Thompson, T. O. Yeates, and D. Eisenberg Visualization and interpretation of protein networks in Mycobacterium tuberculosis based on hierarchical clustering of genome-wide functional linkage maps Nucleic Acids Res., December 15, 2003; 31(24): 7099 - 7109. [Abstract] [Full Text] [PDF]
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R. Jansen, H. Yu, D. Greenbaum, Y. Kluger, N. J. Krogan, S. Chung, A. Emili, M. Snyder, J. F. Greenblatt, and M. Gerstein A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data Science, October 17, 2003; 302(5644): 449 - 453. [Abstract] [Full Text] [PDF]
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