Abstract
In this paper, we have proposed a novel method for the reduction of the number of inferred false positives in gene regulatory networks, constructed from time-series microarray genetic expression datasets. We have implemented a hybrid statistical/swarm intelligence technique for the purpose of reverse engineering genetic networks from temporal expression data. The theory of combination has been used to reduce the search space of network topologies effectively. Recurrent neural networks have been employed to obtain the underlying dynamics of the expression data accurately. Two swarm intelligence techniques, namely, Particle Swarm Optimisation and a Bat Algorithm inspired variant of the same, have been used to train the corresponding model parameters. Subsequently, we have identified and used their common portions to construct a final network where the incorrect predictions have been filtered out. We have done preliminary investigations on experimental (in vivo) data sets of the real-world SOS DNA repair network in Escherichia coli. Furthermore, we have implemented our proposed algorithm on medium-scale networks, consisting of 10 and 20 genes. Experimental results are quite encouraging, and they suggest that the proposed methodology is capable of reducing the number of false positives, thus, increasing the overall accuracy and the biological plausibility of the predicted genetic networks.
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References
McLachlan, G., Do, K.A., Ambroise, C.: Analysing Microarray Gene Expression Data. Wiley, New York (2005)
Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)
Vohradsky, J.: Neural model of the genetic network. J. Biol. Chem. 276(39), 36168–36173 (2001)
Voit, E.O.: Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists. Cambridge University Press, Cambridge (2000)
Hache, H., Lehrach, H., Herwig, R.: Reverse engineering of gene regulatory networks: a comparative study. EURASIP J. Bioinform. Syst. Biol. 1, 1–12 (2009)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43 (1995)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO), vol. 284, pp. 65–74. Springer, Heidelberg (2010)
Kentzoglanakis, K., Poole, M.: A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures. IEEE/ACM Trans. Comput. Biol. Bioinf. 9(2), 358–371 (2012)
Someren, E.V., Wessels, L.F.A., Backer, E., Reinders, M.J.T.: Genetic network modeling. Pharmacogenomics 3(4), 507–525 (2002)
Ronen, M., Rosenberg, R., Shraiman, B.I., Alon, U.: Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc. Nat. Acad. Sci. 99(16), 10555–10560 (2002)
Schaffter, T., Marbach, D., Floreano, D.: GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16), 2263–2270 (2011)
Greenfield, A., Madar, A., Ostrer, H., Bonneau, R.: DREAM4: combining genetic and dynamic information to identify biological networks and dynamical models. PloS One 5(10), e13397 (2010)
Khan, A., Mandal, S., Pal, R.K., Saha, G.: Construction of gene regulatory networks using recurrent neural networks and swarm intelligence. Scientifica 2016(1060843), 14 pages (2016)
Bolouri, H., Davidson, E.H.: Modeling transcriptional regulatory networks. BioEssays 24(12), 1118–1129 (2002)
Chowdhury, A.R., Chetty, M.: Network decomposition based large-scale reverse engineering of gene regulatory network. Neurocomputing 160, 213–227 (2015)
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Khan, A., Saha, G., Pal, R.K. (2016). A Novel Technique for Reduction of False Positives in Predicted Gene Regulatory Networks. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_6
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DOI: https://doi.org/10.1007/978-3-319-44332-4_6
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