Are scale-free networks robust to measurement errors?

BMC Bioinformatics

Volumn 6, Issue , 2005, Pages

  Lin, Nan ^a   Zhao, Hongyu ^b,c  

^a WASHINGTON UNIVERSITY   (United States)

^b YALE UNIVERSITY   (United States)

^c YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX RANDOM NETWORKS; CONNECTIVITY DISTRIBUTION; FALSE POSITIVE AND FALSE NEGATIVES; FALSE POSITIVE RATES; HIGH THROUGHPUT EXPERIMENTS; PROTEIN INTERACTION NETWORKS; SCALE-FREE PROPERTIES; THEORETICAL DERIVATIONS;

ACTIVE NETWORKS; ESTIMATION; MEASUREMENT ERRORS; PARAMETER ESTIMATION; PROTEINS; THROUGHPUT; TOPOLOGY; YEAST;

COMPUTER SIMULATION;

SACCHAROMYCES CEREVISIAE PROTEIN; FUNGAL PROTEIN; PROTEOME;

ANALYTICAL ERROR; ANALYTICAL PARAMETERS; ARTICLE; MATHEMATICAL COMPUTING; NONHUMAN; PROTEIN INTERACTION; SIMULATION; STANDARDIZATION; ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOLOGY; CHEMISTRY; COMPUTER PROGRAM; COMPUTER SIMULATION; METABOLISM; PROCEDURES; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEOMICS; REGRESSION ANALYSIS; REPRODUCIBILITY; SACCHAROMYCES CEREVISIAE;

SACCHAROMYCES CEREVISIAE;

ALGORITHMS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, PROTEIN; FUNGAL PROTEINS; LEAST-SQUARES ANALYSIS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PROTEIN INTERACTION MAPPING; PROTEOME; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SOFTWARE;

EID: 25444526097     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-6-119     Document Type: Article

References (17)

1. Barabási R A L Albert, Jeong H: Scale-free characteristics of random networks: The topology of the World Wide Web. Physica A 2000, 281:69-77.
2. Barabási AL, Jeong H, Néda Z, Revasz E, Schubert A, Vicsek T: Evolution of the social network of scientific collaborations. Physica A 2002, 311:590-614.
3. Jeong H, Mason SP, Barabási AL, Oltvai ZN: Lethality and centrality in protein networks. Nature 2001, 411:41-42.
4. Albert R, Jeong H, Barabási AL: Error and attach tolerance of complex networks. Nature 2000, 406:378-382.
5. Criekinge WV, Beyaert R: Yeast two-hybrid: State of the art. Biol Proced Online 1999, 2:1-38.
6. Deng M, Sun F, Chen T: Assessment of the reliability of protein-protein interactions and protein function prediction. Pac Symp Biocomput 2003, :140-151.
7. Dorogovtsev SN, Mendes JFF: Evolution of Networks. New York: Oxford University Press 2003.
8. Deng M, Mehta S, Sun F, Chen T: Inferring domain-domain interactions from protein-protein interactions. Genome Research 2002, :1540-1548.
9. Rousseeuw PJ, Leroy AM: Robust regression and outlier detection. New York: Wiley 1987.
10. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000, 403:601-603.
11. Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D: DIP: the database of interacting proteins. Nucleic Acids Res 2000, 28:289-291.
12. Y2H protein interaction network data[http://www.nd.edu/~networks/database/protein/bo.dat.gz].
13. MIPS gold standard protein interaction network data [http://hto-b.usc.edu/~msms/AssessInteraction/MIPSMatchYPD.txt].
14. Yeast Proteome Database [http://www.proteome.com/YPDhome.html].
15. Barabási AL, Albert R, Jeong H: Mean-field theory for scale-free random networks. Physica A 1999, 272:173-187.
16. Albert R, Barabási AL: Topology of evolving networks: local events and universality. Phys Rev Lett 2000, 85:5234-5237.
17. R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 2004, [http://www.R-project.org].