A Network-Based Method to Assess the Statistical Significance of Mild Co-Regulation Effects

PLoS ONE

Volumn 8, Issue 9, 2013, Pages

  Horvát, Emoke Ágnes ^a,b   Da Zhang, Jitaovid ^c   Uhlmann, Stefan ^a,d   Sahin, Özgür ^e   Zweig, Katharina Anna ^b  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b University of Koblenz Landau   (Germany)

^c F Hoffmann La Roche   (Switzerland)

^d GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^e UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA; VASCULOTROPIN RECEPTOR;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DATA ANALYSIS; DOWN REGULATION; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN CELL; IN VITRO STUDY; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MOLECULAR DYNAMICS; PROCESS DEVELOPMENT; PROTEIN DETERMINATION; PROTEIN LOCALIZATION; PROTEOMICS; RELIABILITY; SCORING SYSTEM; SENSITIVITY ANALYSIS; SIGNAL TRANSDUCTION; SIGNIFICANT COREGULATION FILTER ALGORITHM; STATISTICAL SIGNIFICANCE; UPREGULATION;

ALGORITHMS; CELL LINE, TUMOR; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; HUMANS; MICRORNAS; PROTEOMICS; RECEPTOR, EPIDERMAL GROWTH FACTOR; SIGNAL TRANSDUCTION;

EID: 84883642750     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0073413     Document Type: Article

References (54)

1. Boldt S, Knops K, Kriehuber R, Wolkenhauer O, (2012) A frequency-based gene selection method to identify robust biomarkers for radiation dose prediction. International Journal of Radiation Biology 88: 267-276.
2. Sahin Ö, Löbke C, Korf U, Appelhans H, Sültmann H, et al. (2007) Combinatorial RNAi for quantitative protein network analysis. Proc Nat Acad Sci 104: 6579-6584.
3. Vasudevan S, Tong Y, Steitz JA, (2007) Switching from repression to activation: microRNAs can up-regulate translation. Science 318: 1931-1934.
4. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, et al. (2002) Frequent deletions and downregulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Nat Acad Sci 99: 15524-15529.
5. Esquela-Kerscher A, Slack FJ, (2006) Oncomirs- microRNAs with a role in cancer. Nature 6: 259-269.
6. He L, He X, Lowe SW, Hannon GJ, (2007) microRNAs join the p53 network-another piece in the tumour-suppression puzzle. Nature Reviews Cancer 7: 819-822.
7. Ma L, Teruya-Feldstein J, Weinberg RA, (2007) Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 449: 682-688.
8. Kota J, Chivukula RR, O'Donnell KA, Wentzel EA, Montgomery CL, et al. (2009) Therapeutic microRNA delivery suppresses tumorigenesis in a murine liver cancer model. Cell 137: 1005-1017.
9. Poliseno L, Salmena L, Zhang J, Carver B, Haveman WJ, et al. (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465: 1033-1038.
10. Lujambio A, Lowe SW, (2012) The microcosmos of cancer. Nature 482: 347-355.
11. miRBase release 19. Available: http://www.mirbase.org/. Accessed 2013 April 15.
12. Satoh J, (2012) Molecular network analysis of human microRNA targetome: from cancers to Alzheimers disease. BioData Mining 5: 1-22.
13. Friedman RC, Farh KKH, Burge CB, Bartel D, (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19: 92-105.
14. Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, et al. (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433: 769-773.
15. Baek D, Villén J, Shin C, Camargo FD, Gygi SP, et al. (2008) The impact of microRNAs on protein output. Nature 455: 64-71.
16. Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, et al. (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455: 58-63.
17. Leivonen SK, Mäkelä R, Östling P, Kohonen P, Haapa-Paananen S, et al. (2009) Protein lysate microarray analysis to identify microRNAs regulating estrogen receptor signaling in breast cancer cell linesmicrornas regulating er. Oncogene 28: 3926-3936.
18. Malo N, Hanley J, Cerquozzi S, Pelletier J, Nadon R, (2006) Statistical practice in high-throughput screening data analysis. Nat Biotechnol 24 (2): 167-175.
19. Murphy D, (2002) Gene expression studies using microarray: principles, problems, and prospects. Advances in Physiology Education 26: 256-270.
20. Uhlmann S, Mannsperger H, Zhang JD, Horvát EÁ, Schmidt C, et al. (2012) Global miRNA regulation of a local protein network: Case study with the EGFR-driven cell cycle network in breast cancer. Molecular Systems Biology 570: 8.
21. Stuart JM, Segal E, Koller D, Kim SK, (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302: 249-255.
22. Quackenbush J, (2003) Microarrays-guilt by association. Science 302: 240-241.
23. Neeley ES, Kornblau SM, Coombes KR, Baggerly KA, (2009) Variable slope normalization of reverse phase protein arrays. Bioinformatics 25 (11): 1384-1389.
24. Wasserman S, Faust K (1999) Social Network Analysis - Methods and Applications. Cambridge University Press, Cambridge, revised, reprinted edition.
25. Barabási AL, Gulbahce N, Loscalzo J, (2011) Network medcine: a network-based approach to human disease. Nature 12: 56-68.
26. Milo R, Shen-Orr SS, Itzkovitz S, Kashtan N, Chklovskii D, et al. (2004) Network motfis: Simple building blocks of complex networks. Science 298: 824-827.
27. Shen-Orr SS, Milo R, Mangan S, Alon U, (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31: 64-68.
28. Zweig KA (2010) How to forget the second side of the story: A new method for the one-mode projection of bipartite graphs. In: Proceedings of the second International Conference on Advances in Social Network Analysis and Mining (ASONAM'10). IEEE Computer Society, 200-207.
29. Zweig KA, Kaufmann M, (2011) A systematic approach to the one-mode projection of bipartite graphs. Social Network Analysis and Mining 1: 187-218.
30. Cobb GW, Chen YP, (2003) An application of Markov Chain Monte Carlo to community ecology. The American Mathematical Monthly 110: 265-288.
31. Brualdi RA, (2006) Algorithms for constructing (0,1)-matrices with prescribed row and column sum vectors. Discrete Mathematics 306: 3054-3062.
32. Gionis A, Mannila H, Mielikäinen T, Tsaparas P (2007) Assessing data mining results via swap randomization. In: ACM Transactions on Knowlwdge Discovery from Data. 167-176.
33. Girvan M, Newman ME, (2002) Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99: 7821-7826.
34. Brandes U, Gaertler M, Wagner D (2007) Engineering graph clustering: Models and experimental evaluation. ACM Journal of Experimental Algorithmics 12: Article 1.1.
35. Brandes U, Gaertler M, Wagner D (2003) Experiments on graph clustering algorithms. In: Proceedings of the 11th European Symposium on Algorithms.
36. Liben-Nowell D, Kleinberg J, (2007) The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58: 1019-1031.
37. Leicht EA, Holme P, Newman MEJ, (2006) Vertex similarity in networks. Physical Review E 73: 026120.
38. Freeman LC, (1992) The sociological concept of "group": An empirical test of two models. American Journal of Sociology 98: 152-166.
39. Zahoránszky L, Katona G, Hári P, Málnási-Csizmadia A, Zweig K, et al. (2009) Breaking the hierarchy - a new cluster selection mechanism for hierarchical clustering methods. Algorithms for Molecular Biology 4: 12.
40. Serrano MÁ, Boguñá M, Vespignani A, (2009) Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences 106: 6483-6488.
41. Watts DJ, Strogatz SH, (1998) Collective dynamics of 'small-world' networks. Nature 393: 440-442.
42. Inui M, Martello G, Piccolo S, (2010) MicroRNA control of signal transduction. Nature Reviews Molecular Cell Biology 11: 252-263.
43. Enright AJ, John B, Gaul U, Tuschl T, Sander C, et al. (2003) MicroRNA targets in Drosophila. Genome Biology 5: R1.
44. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, et al. (2005) Combinatorial microRNA target predictions. Nature Genetics 37: 495-500.
45. Wu S, Huang S, Ding J, Zhao Y, Liang L, et al. (2010) Multiple microRNAs modulate p21Cip1/Waf1 expression by directly targeting its 3′ untranslated region. Oncogene 29: 2302-2308.
46. The ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57-74.
47. Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, et al. (2012) Architecture of the human regulatory network derived from ENCODE data. Nature 489: 91-100.
48. Tilgner H, Knowles DG, Johnson R, Davis CA, Chakrabortty S, et al. (2012) Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs. Genome Res 22: 1616-1625.
49. Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M, (2012) Linking disease associations with regulatory information in the human genome. Genome Res 22: 1748-1759.
50. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Nat Acad Sci 102: 15545-15550.
51. Zhang B, Horvath S, (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mo B 4: 1544-6115.
52. Voineagu I, Wang X, Johnston P, Lowe J, Tian Y, et al. (2011) Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474: 380-384.
53. Marco A, Konikoff C, Karr TL, Kumar S, (2009) Relationship between gene co-expression and sharing of transcription factor binding sites in Drosophila melanogaster. Bioinformatics 25: 2473-2477.
54. Horvát EÁ, Zweig KA (2012) One-mode projection of multiplex bipartite graphs. In: Proceedings of the second International Conference on Advances in Social Network Analysis and Mining (ASONAM'12). IEEE Computer Society, 598-605.