Abstract
In recent years, evolutionary methods have seen unprecedented success in elucidation of gene networks, especially from microarray data. We have implemented the Phenomic Algorithm which is an evolutionary method for inference of gene networks based on population dynamics. We have used S-systems to model gene interactions and applied memetic search to fine tune the parameters of the inferred networks. We have tested the novel algorithm on artificial gene expression datasets obtained from simulated gene networks. We have also compared the results to those obtained from two other similar algorithms. Results showed that the new method, which we call as Phenomic Algorithm with Memetic Search (PAMS), is an effective method for inference of gene networks.
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References
Schulze, A., Downward, J.: Navigating Gene Expression Using Microarrays – A Technology Review. Nature Cell Biology 3, E190–E195 (2001)
Soinov, L.A., Krestyaninova, M.A., Brazma, A.: Towards Reconstruction of Gene Networks from Expression Data by Supervised Learning. Genome Biology 4(1), R6 (2003)
D’haeseleer, P., Liang, S., Somogyi, R.: Gene Expression Analysis and Genetic Network Modelling: Tutorial. In: Pacific Symposium on Biocomputing (1999)
D’Souza, R.G.L., Chandra Sekaran, K., Kandasamy, A.: A Phenomic Algorithm for Reconstruction of Gene Networks. In: IV International Conference on Computational Intelligence and Cognitive Informatics, CICI 2007, pp. 53–58. WASET, Venice (2007)
D’Souza, R.G.L., Chandra Sekaran, K., Kandasamy, A.: Reconstruction of Gene Networks using Phenomic Algorithms. Intl. Jour. of Artificial Intell. and Appl. 1(2) (2010)
Somogyi, R., Fuhrman, S., Askenazi, M., Wuensche, A.: The Gene Expression Matrix: Towards the Extraction of Genetic Network Architectures. In: Proc. of Second World Cong. of Nonlinear Analysts (WCNA 1996), vol. 30(3), pp. 1815–1824 (1997)
Akutsu, T., Miyano, S., Kuhara, S.: Identification of Genetic Networks from a Small Number of Gene Expression Patterns under the Boolean Network Model. In: Pacific Symp. on Biocomputing, vol. 4, pp. 17–28 (1999)
D’haeseleer, P., Liang, S., Somogyi, R.: Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering. Bioinformatics 16(8), 707–726 (2000)
Savageau, M.A.: Power-law Formalism: A Canonical Nonlinear Approach to Modelling and Analysis. In: Proc. of the World Congress of Nonlinear Analysts 1992, pp. 3323–3334 (1995)
Spieth, C., Streichert, F., Speer, N., Zell, A.: Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 461–470. Springer, Heidelberg (2004)
Ahmed, A., Song, L., Xing, E.: Time-Varying Networks: Reconstructing Temporally Rewiring Genetic Interactions during the Life Cycle of Drosophila melanogaster. CMU-MLD Technical Report CMU-ML-08-118 (2008)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation 8(2), 125–147 (2000)
A Collection of Artificial Gene Networks (2010), http://www.comp-sys-bio.org/AGN/data.html (accessed January 20, 2010)
Supper, J., Fröhlich, H., Spieth, C., Dräger, A., Zell, A.: Inferring Gene Regulatory Networks by Machine Learning Methods. In: APBC 2007, pp. 247–256 (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II. IEEE Trans. of Evol. Comp. 6(2), 182–197 (2002)
Spieth, C., Streichert, F., Speer, N., Zell, A.: Multi-objective Model Optimization for Inferring Gene Regulatory Networks. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 607–620. Springer, Heidelberg (2005)
Spieth, C., Streichert, F., Speer, N., Zell, A.: A Memetic Inference Method for Gene Regulatory Networks Based on S-Systems. In: Congress on Evol. Comp (CEC 2004), Proc. Part I, pp. 152–157. IEEE Press (2004)
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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D’Souza, R.G.L., Sekaran, K.C., Kandasamy, A. (2012). A Phenomic Algorithm for Inference of Gene Networks Using S-Systems and Memetic Search. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_24
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DOI: https://doi.org/10.1007/978-3-642-32615-8_24
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