Biological feature validation of estimated gene interaction networks from microarray data: A case study on MYC in lymphomas

Briefings in Bioinformatics

Volumn 12, Issue 3, 2011, Pages 230-244

  Mansmann, Ulrich ^a   Jurinovic, Vindi ^b  

^a UNIVERSITY OF MUNICH   (Germany)

^b NONE

Author keywords

Biological knowledge; Comparing networks; Gene expression; Interaction networks

Indexed keywords

ARTICLE; BIOLOGY; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETIC DATABASE; GENETICS; HUMAN; LYMPHOMA; ONCOGENE MYC; SYSTEMS BIOLOGY; VALIDATION STUDY;

COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENES, MYC; HUMANS; LYMPHOMA; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SYSTEMS BIOLOGY;

EID: 79955781020     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr007     Document Type: Article

References (35)

1. Schäfer J, Strimmer K. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol 2005;4. Article 32.
2. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 2007;9: 432-41.
3. Banerjee O, Ghaoui LE, d'Aspremont A. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. J Machine Learn Res 2008;9: 485-516.
4. Kalisch M, Bühlmann P. Estimating high dimensional acyclic graphs with the PC algorithm. J Machine Learn Res 2007; 8:613-36.
5. Tenenhaus A, Guillemot V, Gidrol X, et al. Gene association networks from microarray data using a regularized estimation of partial correlation based on PLS regression. IEEE/sACMTrans Comput Biol Bioinform 2010;7:251-62.
6. Wang M, Augusto Benedito V, Xuechun Zhao P, et al. Inferring large-scale gene regulatory networks using a low-order constraint-based algorithm. Mol Biosyst 2010;6: 988-98.
7. Friedman N, Linial M, Nachman I, et al. Using Bayesian networks to analyze expression data. J Comput Biol 2000;7: 601-20.
8. Krämer N, Schäfer J, Boulesteix AL. Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics 2009;10:384.
9. Dougherty ER. Validation of gene regulatory networks. Sci Inf Brief Bioinform 2010. Dec 22 [Epub ahead of print 22 December 2010].
10. Basso K, Margolin AA, Stolovitzky G, et al. Reverse engineering of regulatory networks in human B cells. Nature Genetics 2005;37:382-90.
11. Hummel M, Bentink S, Berger H, et al. A biologic definition of Burkitt's lymphoma from transcriptional and genomic profiling. N Engl. J Med 2006;354:2419-30.
12. Magolin AA, Califano A. Theory and limitations of genetic network inference from microarray data. Ann NY Acad Sci 2007;1115:51-72.
13. Meinshausen N, Bühlmann P. High-dimensional graphs and variable selection with the Lasso. Ann Statistics 2006; 34:1436-62.
14. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan San Francisco: Kaufmann Publishers, 1988.
15. Gill R, Datta S, Datta S. A statistical framework for differential network analysis from microarray data. BMC Bioinformatics 2010;11:95.
16. Kostka D, Spang R. Finding disease specific alterations in the co-expression of genes. Bioinformatics 2004;20(Suppl 1): i194-9.
17. Choi JK, Yu U, Yoo OJ, et al. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 2005;21:4348-55.
18. Gillis J, Pavlidis P. A methodology for the analysis of differential coexpression across the human lifespan. BMC Bioinformatics 2009;10:306.
19. Mansmann U, Schmidberger M, Strobl R, et al. Indirect comparison of interaction graphs. In: Kneib T, Tutz G (eds). StatisticalModelling and Regression Structures, Festschrift in Honour of Ludwig Fahrmeir. Berlin: Springer, 2010;249-65.
20. Boxer LM, Dang CV. Translocations involving c-myc and c-myc function. Oncogene 2001;20:5595-610.
21. Janz S. Myc translocations in B cell and plasma cell neoplasms. DNA Repair 2006;5:1213-24.
22. Schuurman N, Leszczynski A. Ontologies for bioinformatics. BBI 2008;2:187-200.
23. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS 2005; 102:15545-50.
24. Goeman JJ, Mansmann U. Multiple testing on the directed acyclic graph of gene ontology. Bioinformatics 2008;24: 537-44.
25. Jelier R, Goeman JJ, Hettne KM, et al. Literature-aided interpretation of gene expression data with the weighted global test. Brief Bioinform 2010. [Epub ahead of print 9 February 2011].
26. Binder H, Schumacher M. Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics 2009;10:18.
27. Miller C, Schwalb B, Maier K, et al. Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast. Mol Syst Biol 2011;7:458.
28. Ress A, Moelling K. Bcr is a negative regulator of the Wnt signalling pathway. EMBO Rep 2005;6:1095-100.
29. Goeman JJ, van de Geer SA, de Kort F, et al. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 2004;20:93-9.
30. Zheng N, Schulman BA, Song L, et al. Structure of the Cul1-Rbx1-Skp1-F boxSkp2 SCF ubiquitin ligase complex. Nature 2002;416:703-9.
31. Peifer M, Polakis P. Wnt signaling in oncogenesis and embryogenesis- a look outside the nucleus. Science 2000;287: 1606-9.
32. Breiman L. Statistical modelling: the two cultures. Stat Sci 2001;16:199-215.
33. Hjorth JSU. Computer Intensive Statistical Methods:Validation, Model Selection, and Bootstrap. New York: Chapman & Hall, 1994.
34. Li CY, Mao M, Wei L. Genes and (common) pathways underlying drug addiction. PLoS Comput Biol 2008;4:e2.
35. Alexa A, Rahnenführer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 2006;22: 1600-7.