A neural network based modeling and validation approach for identifying gene regulatory networks

Neurocomputing

Volumn 73, Issue 13-15, 2010, Pages 2419-2429

  Knott, S ^a   Mostafavi, S ^b   Mousavi, P ^c  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

^c QUEEN S UNIVERSITY   (Canada)

Author keywords

Gene regulatory networks; Neural networks; Reverse engineering

Indexed keywords

ANIMAL MODEL; BIOLOGICAL DATA; COMPUTATIONAL APPROACH; DATA SETS; GENE EXPRESSION DATA; GENE REGULATORY NETWORKS; MODELING FRAMEWORKS; REGULATORY ELEMENTS; REGULATORY INTERACTIONS; STOCHASTIC SAMPLING; TARGET GENE EXPRESSION;

COMPUTER SIMULATION; GENE EXPRESSION; LAWS AND LEGISLATION; REENGINEERING; REVERSE ENGINEERING; SENSITIVITY ANALYSIS; STATISTICAL TESTS;

NEURAL NETWORKS;

ACCURACY; ANALYTIC METHOD; ARTICLE; ARTIFICIAL DATASET; CALCULATION; CONTROLLED STUDY; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE INTERACTION; GENE REGULATORY NETWORK; GENE TARGETING; HIPPOCAMPUS DEVELOPMENT DATASET; PREDICTION; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SIMULATION; STOCHASTIC MODEL; VALIDITY;

EID: 77955315182     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.04.018     Document Type: Article

References (42)

1. S. Ando, H. Iba, Inference of gene regulatory models by genetic algorithms, in: Proceedings of Genetic and Evolutionary Computation Conference, San Francisco, CA, 2001.
2. Arnon M., Davidson E. The hardwiring of development: organization and function of genomic regulatory systems. Development 1997, 124:1851-1864.
3. Asadaa H., Kawamuraa Y., Maruyamaa K., Kumea H., Dinga R., Jia F., Kanbaraa N., Kuzumea H., Sanbob M., Yagib T., Obata K. Mice lacking 65kDa isoforms of glutamic acid decarboxylase (gad65) maintain normal levels of gad67 and gaba. Biochemical and Biophysical Research Communications 1996, 229:891-895.
4. Barrett C., Palsson B. Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach. PLOS Computation Biology 2006, 2(5):429-438.
5. Bornholdt S. Boolean network models of cellular regulation: prospects and limitation. Journal of the Royal Society, Interface 2008, 5:S85-S94.
6. A. Butte, I. Kohane, Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements, in: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, 2000.
7. T. Chen, H. He, G. Church, Modeling gene expression with differential equations, in: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, 1999.
8. H. DeJong, M. Page, Qualitative simulation of large and complex genetic regulation systems, in: Proceedings of European Conference on Artificial Intelligence, Berlin, Germany, 2000.
9. X. Deng, H. Ali, A computational approach to reconstructing gene regulatory networks, in: Proceedings of the IEEE Computer Society Bioinformatics Conference, Stanford, CA, 2003.
10. P. D'haeseleer, X. Wen, S. Fuhrman, R. Somogyi, Linear modeling of mRNA expression levels during CNS development and injury, in: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, 1999.
11. Fofanov Y., Pettit B.M. Reconstruction of the genetic regulatory dynamics of the rat spinal cord development: local invariants approach. Journal of Biomedical Informatics 2002, 5-6:343-351.
12. F. Forsee, M. Hagan, Gauss network approximation to Bayesian learning, in: Proceedings of the International Joint Conference on Neural Networks, 1997.
13. Friedman N., Linial M., Nachman I., Peer D. Using Bayesian networks to analyze expression data. Journal of Computational Biology 2000, 7(6):601-620.
14. R. Gencay, M. Qi, Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging, IEEE Transactions on Neural Networks 12 (4) (2001) 726-734.
15. M. Hagan, M. Menhaj, Training feed-forward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks 5 (6) (1994) 989-993.
16. Hartwell L., Hood L., Goldberg M., Reynolds A., Silver L., Veres R. Genetics: From Genes to Genomes 2008, McGraw-Hill. third ed.
17. Hecker M., Lambeck S., Toepfer S., van Someren E., Guthke R. Gene regulatory network inference: data integration in dynamic models-a review. BioSystems 2009, 96:86-103.
18. Hunter L. Molecular biology for computer scientists. Artificial Intelligence for Molecular Biology 1993, 1-46.
19. Husmeier D. Reverse engineering of genetic networks with Bayesian networks. Biochemical Society Transactions 2003, 6:1516-1518.
20. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 2003, 19(17):2271-2282.
21. Jong H.D. Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 2002, 9(1):67-103.
22. Karlebach G., Shamir R. Modeling and analysis of gene regulatory networks. Nature Reviews, Molecular Cell Biology 2008, 9(10):770-780.
23. Kauffman S. Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology 1969, 22:437-467.
24. Keedwell E., Narayanan A. Genetic algorithms for gene expression analysis. Proceedings of the First European Workshop on Evolutionary Bioinformatics, Lecture Notes in Computer Science, vol. 2611 2003, Springer, Berlin, Heidelberg, Germany. G. Raidle (Ed.).
25. E. Keedwell, A. Narayanan, D. Savic, Constructing gene regulatory networks using artificial neural networks, in: Proceedings of the International Joint Conference on Neural Networks, Hawaii, 2002.
26. Applied Cell and Molecular Biology for Engineers 2007, McGraw-Hill Professional. M. King, L. Waite (Eds.).
27. A. Krishna, A. Narayanan, E. Keedwell, Reverse engineering gene networks with artificial neural networks, in: B. Ribeiro et al. (Eds.), Proceedings of the International Conference on Adaptive and Natural Computing Algorithms, Springer, 2005.
28. S. Liang, S. Fuhrman, R. Somogyi, Reveal, a general reverse engineering algorithm for inference of genetic network architectures, in: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, 1998.
29. D. MacKay, Bayesian interpolation, Neural Computation 4 (3) (1992) 415-447.
30. Margolin A., Nemenman I., Basso K., Klein U., Wiggins C., Stolovitzky G., Favera R., Califano A. Aracne: an algorithm for reconstruction of genetic networks in a mammalian cellular context. BMC Bioinformatics 2006, 7(Suppl. 1):S7.
31. K. Obata, in: E.R. Kandal (Ed.), Handbook of Physiology: The Nervous System, vol. 1, American Physiological Society, Bethesda, MD, 1996.
32. Opgen-Rhein R., Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high dimensional plant gene expression data. BMC Systems Biology 2007, 1:37.
33. Schadt E., Lum P. Reverse engineering gene networks to identify key drivers of complex disease phenotypes, Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Journal of Lipid Research 2006, 47(12):2601-2613.
34. Sima C., Hua J., Jung S. Inference of gene regulatory networks using time-series data: a survey. Current Genomics 2009, 10(9):416-429.
35. Smith V., Jarvis E., Hatermink A. Evaluating functional network inference using simulation of complex biological systems. Bioinformatics 2002, 18(Suppl. 1):S216-S224.
36. Stuart J., Segal E., Koller D., Kim S. A gene-coexpression network for global discovery of conserved genetic modules. Science 2003, 5643:249-255.
37. van Someren E., Wessels L., Backer E., Reinders M. Genetic network modeling. Pharmacogenomics 2002, 3(4):525-607.
38. Wahde M., Hertz J. Coarse-grained reverse engineering of genetic regulatory networks. Biosystems 2000, 55:129-136.
39. Wang R., Wang Y., Zhang X., Chen L. Inferring transcriptional regulatory networks from high-throughput data. Bioinformatics 2007, 23(22):3056-3964.
40. Wen X., Fuhrman S., Michales G., Carr D., Smith S., Barker J., Somogyi R. Large-scale temporal gene expression mapping of central nervous system development. Proceedings of the National Academy of Science 1998, 95:334-339.
41. Werhli A., Husmeier D. Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology 2007, 6(1). (Article 1).
42. Yeung M., Tegner J., Collins J. Reverse engineering gene networks using singular value decomposition and robust regression. Proceedings of the National Academy of Sciences 2002, 99(9):6163-6168.