Summary

Proceedings of the 2012 International Symposium on Nonlinear Theory and its Applications

2012

Session Number:C2L-B

Session:

Number:648

The Parameter Optimization in the Inference of Gene Regulatory Network by Neural Networks Adopting Majority Rule

Yasuki Hirai,  Naoyuki Kizaki,  Hiroshi Yoshino,  Hiroaki Kurokawa,  

pp.648-651

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.1.648

PDF download (575.4KB)

In order to infer the Gene Regulatory Network (GRN) described by the differential equation, it is required that the function approximation of a lot of unknown functions using the time course data of gene expressions. Recently, the inferring method of the GRN using neural network had been proposed. We also had been proposed another method using neural networks that can show the various results depending on the parameter that defined in our method. Although our method can show the preferable results depending on the requirement, it cannot decide the parameter to show the reasonable result of the inference automatically. In this paper, we propose the method to decide the parameter for the GRN inference using our method. In simulations, the results show that the method can decide the parameter appropriately, and the reasonable result of the inference is obtained.

[1] D’haeseleer, P. and Liang, S. and Somogyi, R., ”Genetic network inference: from co-expression clustering to reverse engineering”, Bioinformatics 16, No.8, pp.707-726, 2000

[2] T. Akutsu and S. Miyano, ”Identification of genetic networks from a small numbaer of gene expression pataerns under the boolean network model”, 1999 Pacific Symposium on Biocomputing, pp.17-28, 1999.

[3] Dirk Husmeier, ”Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks”, Bioinformatics, Vol.19, no.17, pp. 2271-2282, 2003.

[4] S. Kikuchi, D. Tominaga, M. Arita, K.Takahashi and M.Tomita, ”Dynamic modeling of genetic networks using genetic algorithm and S-system”, Bioinformatics, Vol.19, no.5, pp. 643-650, 2003.

[5] N. Noman and H. Iba, ”Inferring Gene Regulatory Networks Using Differential Evolution with Local Search Heuristics”, IEEE/ACM Transactions on computational biology and bioinformatics, Vol. 4, No. 4, pp.634-647, 2007

[6] S. Kimura, K.Sonoda, S. Yamane, K. Matsumura and M. Hatakeyama, ”Function approximation approach to the inference of neural network models of genetic networks” IPSJ Transactions on Bioinformatics, Vol.48, No.SIG5(TBIO2), pp.9-19, 2007.

[7] B. S. Mondal, A.K. Sarkar, M.M. Hasan and N. Noman, ”Reconstruction of Gene Regulatory Networks using Differential Evolution,” Proc. of ICCTT2010, pp.440-445, 2010.

[8] M. Kabir, N. Noman and H. Iba, ”Reverse engineering gene regulatory network from microarray data using linear time-variant model,” BMC bioinformatics, 11(Suppl 1):S56, 2010.

[9] Y. Hirai, M. Kikuchi, and H.kurokawa, ”Inferring method of the Gene Regulatory Networks using Neural Networks Adopting a Majority Rule,” Proc. of IJCNN2011, pp. 2936-2943, 2011.

[10] ”Data of SOS system reporter plasmids” http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/