Validation of gene regulatory networks: Scientific and inferential

Briefings in Bioinformatics

Volumn 12, Issue 3, 2011, Pages 245-252

  Dougherty, Edward R ^a,b  

^a Texas AandM University   (United States)

^b TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

Author keywords

Epistemology; Gene network; Genomics; Systems biology; Validation

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGY; GENE REGULATORY NETWORK; METHODOLOGY; SYSTEMS BIOLOGY; VALIDATION STUDY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENE REGULATORY NETWORKS; SYSTEMS BIOLOGY;

EID: 79955765957     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbq078     Document Type: Article

References (35)

1. Shmulevich I, Dougherty ER. Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks. New York: SIAM Press, 2010.
2. de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. Comput Biol 2002;9:67-103.
3. van Someren EP, Wessels LFA, Backer E, Reinders MJT. Genetic network modeling. Pharmacogenomics 2002;3: 507-25.
4. Barabasi A-L, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet 2004;5: 101-13.
5. Shmulevich I, Dougherty ER. Genomic Signal Processing. Princeton: Princeton University Press, 2007.
6. Cho KH, Choo SM, Jung SH, et al. Reverse engineering of gene regulatory networks. IETSyst Biol 2007;1:149-63.
7. Schlitt T, Brazma A. Current approaches to gene regulatory network modeling. BMCBioinformatics 2007;8(Suppl. 6):S9.
8. Sima C, Hua J, Jung S. Inference of gene regulatory network using time-series data: a survey. Curr Genomics 2009; 10:416-29.
9. Dougherty ER. Validation of inference procedures for gene regulatory networks. Curr Genomics 2007;8:351-9.
10. Kauffman SA. Metabolic stability and epigenesis in randomly constructed genetic nets. Theor Biol 1969;22: 437-67.
11. Marshall S, Yu L, Xiao Y, Dougherty ER. Inference of probabilistic Boolean networks from a single observed temporal sequence. EURASIP J Bioinformatics Syst Biol 2007. Article ID 32454.
12. Dougherty ER, Braga-Neto U. Epistemology of computational biology: mathematical models and experimental prediction as the basis of their validity. BiolSyst 2006;14:65-90.
13. Einstein A. The library of living philosophers. In: Schilpp PA, (ed). The Philosophy of Bertrand Russell. Evanston and Chicago: Northwestern University, 1944;289.
14. de Jong H, Geiselmann J, Hernandez C, Page M. Genetic network analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 2003;19:36-344.
15. Batt G, Ropers D, de Jong H, et al. Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli. Bioinformatics 2005;21(Suppl. 1):19-28.
16. Zhao W, Serpedin E, Dougherty ER. Inferring connectivity of genetic networks using information theoretic criteria. IEEE/ACM Trans Comput Biol Bioinform 2008;5: 262-74.
17. Liang S, Fuhrman S, Somogyi R. REVEAL a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 1998;3:18-29.
18. Margolin A, Nemenman I, Basso K, et al. ARACNE: an algorithm for reconstruction of genetic networks in a mammalian cellular context. BMC Bioinformatics 2006; 7(Suppl. 1):S7.
19. Chen X, Anantha G, Wang X. An effective structure learning method for constructing gene networks. Bioinformatics 2006;22:1367-74.
20. Zou M, Conzen SD. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 2005;21: 71-9.
21. Bansal M, Belcastro V, Ambesi-Implombato A, di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol 2006;3:78.
22. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 2003;19: 2271-82.
23. Werhli AV, Grzegorczyk M, Husmeier D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 2006;22:2523-31.
24. Marbach D, Prill RJ, Schaffter T, et al. Revealing strengths and weaknesses of methods for gene network inference. ProcNatl Acad SciUSA 2010;107:6286-91.
25. Mehta T, Murat T, Allison DB. Towards sound epistemological foundations of statistical methods for highdimensional biology. Nat Genet 2004;36:943-7.
26. Hanczar B, Hua J, Dougherty ER. Decorrelation of the true and estimated classifier errors in high-dimensional settings. EURASIP J Bioinformatics Syst Biol 2007. Article ID 38473.
27. Braga-Neto UM. Fads and fallacies in the name of small-sample microarray classification. IEEE Signal Process Mag 2007;24:91-9.
28. Keller EF. Revisiting scale-free networks. BioEssays 2005; 27:1060-8.
29. Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. JNatl Cancer Inst 2007;99:147-57.
30. Brun M, Sima C, Hua J, et al. Model-based evaluation of clustering validation measures. Pattern Recogn 2007;40: 807-24.
31. Dougherty ER. On the epistemological crisis in genomics. Curr Genomics 2008;9:69-79.
32. Boulesteix A-L, Strobl C. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction. BMCMed ResMethodol 2009; 9:85.
33. Boulesteix AL. Over-optimism in bioinformatics research. Bioinformatics 2010;26:437-9.
34. Yousefi MR, Hua J, Sima C, Dougherty ER. Reporting bias when using real data sets to analyze classification performance. Bioinformatics 2010;26:68-76.
35. Jelizarow M, Guillemot V, Tenenhaus A, et al. Over optimism in bioinformatics: an illustration. Bioinformatics 2010; 26:1990-98.