Discriminative topological features reveal biological network mechanisms

BMC Bioinformatics

Volumn 5, Issue , 2004, Pages

  Middendorf, Manuel ^a   Ziv, Etay ^b   Adams, Carter ^a   Hom, Jen ^a   Koytcheff, Robin ^a   Levovitz, Chaya ^c   Woods, Gregory ^a   Chen, Linda ^a   Wiggins, Chris ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b NONE   (United States)

^c Barnard College of Columbia University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION SCHEME; CLUSTERING COEFFICIENT; DEGREE DISTRIBUTIONS; DIFFERENT MECHANISMS; DISCRIMINATIVE CLASSIFIERS; PREFERENTIAL ATTACHMENT MODEL; PROTEIN INTERACTION NETWORKS; TOPOLOGICAL FEATURES;

ALGORITHMS; BIOINFORMATICS; ESCHERICHIA COLI; NEURONS;

LARGE SCALE SYSTEMS;

SACCHAROMYCES CEREVISIAE PROTEIN;

ARTICLE; CAENORHABDITIS ELEGANS; COMPUTER PROGRAM; CONTROLLED STUDY; ESCHERICHIA COLI; GENE CONSTRUCT; GENE MAPPING; GENE MUTATION; GENETIC ALGORITHM; GENETIC TRANSCRIPTION; MATHEMATICAL COMPUTING; MICROBIAL GENETICS; NERVE CELL NETWORK; NONHUMAN; PROTEIN INTERACTION; SACCHAROMYCES CEREVISIAE; ANIMAL; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOLOGY; ESCHERICHIA COLI K 12; GENETICS; METABOLISM; PHYSIOLOGY; PROCEDURES; PROTEIN ANALYSIS;

ANIMALS; CAENORHABDITIS ELEGANS; COMPUTATIONAL BIOLOGY; ESCHERICHIA COLI K12; MODELS, BIOLOGICAL; MODELS, GENETIC; MODELS, NEUROLOGICAL; NERVE NET; NEURAL NETWORKS (COMPUTER); PROTEIN INTERACTION MAPPING; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS;

EID: 13244299158     PISSN: 14712105     EISSN: None     Source Type: Journal    
DOI: 10.1186/1471-2105-5-181     Document Type: Article

References (30)

1. Faloutsos C, Faloutsos M, Faloutsos P: On power-law relationships of the internet topology. Computer Communications Review 1999, 29:251-262.
2. Albert R, Jeong H, Barabási AL: Diameter of the world-wide web. Nature 1999, 401:130-131.
3. Newman M: The Structure and Function of Complex Networks. SIAM 2003, 45:167.
4. Milo R, Shen-Orr SS, Itzkovitz S, Kashtan N, Alon U: Simple building blocks of complex networks. Science 2002, 298:824-7.
5. Wuchty S, Oltvai ZN, Barabási AL: Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat gen 2003, 35:176-9.
6. Ziv E, Koytcheff R, Wiggins CH: Novel systematic discovery of statistically significant network features. arXiv:cond-mat/0306610 2003.
7. Artzy-Randrup Y, Fleishman SJ, Ben-Tal N, Stone L: Comment on "Network Motifs: Simple Building Blocks of Complex Networks" and "Superfamilies of Evolved and Designed Networks". Science 2004, 305:1107.
8. Milo R, Itzkovitz S, Kashtan N, Levitt R, Alon U: Response to Comment on "Network Motifs: Simple Building Blocks of Complex Networks" and "Superfamilies of Evolved and Designed Networks". Science 2004, 305:1107d.
9. Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, Ayzenshtat I, Sheffer M, Alon U: Superfamilies of evolved and designed networks. Science 2004, 303:1538.
10. Shen-Orr S, Milo R, Mangan S, Alon U: Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 2002, 31:64-68.
11. Jeong H, Mason S, Barabási A, Oltvai ZN: Lethality and centrality of protein networks. Nature 2001, 411:41-42.
12. White JG, Southgate E, Thompson JN, Brenner S: The structure of the nervous system of the nematode C. elegans. Phil Trans of the Royal Society of London 1986, 314:1-340.
13. Kumar R, Raghavan P, Rajagopalan S, Sivakumar D: Stochastic models for the web graph. In Symposium on Foundations of Computer Science FOCS, IEEE 2000.
14. [http://www.columbia.edu/itc/applied/wiggins/netclass].
15. Sole RV, Pastor-Satorras R, Smith E, Kepler TB: A model of largescale proteome evolution. Advances in Complex Systems 2002, 5(43):.
16. Vazquez A, Flammini A, Maritan A, Vespignani A: Modeling of protein interaction networks. ComPlexUs 2003, 1(38):.
17. Grindrod P: Range-Dependent Random Graphs and their application to modeling large small-world proteome datasets. Phys Rev E Stat Nonlin Soft Matter Phys 2002, 66:066702.
18. Krapivsky PL, Rodgers GJ, Redner S: Degree distributions of growing networks. Phys Rev Lett 2001, 86:5401-5404.
19. Kim DH, Kahng B, Kim D: The q-component static model: modeling social networks. arXiv:cond-mat/0307184 2003.
20. Goh KI, Kahng B, Kim D: Universal behavior of load distribution in scale-free networks. Phys Rev Lett 2001, 87: 78701.
21. Caldarelli G, Capocci A, Rios PDL, Munoz AM: Scale-free networks from varying vertex intrinsic fitness. Phys Rev Lett 2002, 89:258702.
22. Watts D, Strogatz S: Collective dynamics of small-world networks. Nature 1998, 393:202-204.
23. Erdös P, Rényi A: On random graphs. Publicationes Mathematicae 1959, 6:290-297.
24. Bianconi G, Barabási A: Competition and multiscaling in evolving networks. Europhys Lett 2001, 54:436-442.
25. Barabási A: Emergence of scaling in random networks. Science 1999, 286:509-512.
26. Callaway D, Hopcroft JE, Kleinberg JM, Newman ME, Strogatz SH: Are randomly grown graphs really random? Phys Rev E Stat Nonlin Soft Matter Phys 2001, 64:041902.
27. Higham JD: Spectral Reordering of a Range-Dependent Weighted Random Graph. Mathematics Research Report 14, University of Strathclyde 2003.
28. Vazquez A: Knowing a network by walking on it: emergence of scaling. arXiv:cond-mat/0006132 2002.
29. Vapnik V: The Nature of Statistical Learning Theory Springer-Verlag, NY, USA; 1995.
30. Joachims T: Making large-Scale SVM Learning Practical. In In Advances in Kernel Methods - Support Vector Learning Edited by: Schölkopf B, Burges C, Smola A. MIT-Press; 1999.