Design and Construction of a Yeast Gene Bayesian Regulatory Network

Abstract:

At present, biological data provide possibility to further data analysis data, improving deep understanding on complicated regulatory model. Thanks to the study of model organism yeast and the construction of its gene regulatory network, we can improve the diagnosis and treatment level of the human polygene hereditary diseases which are similar to yeast genes. Among a variety of gene regulatory network models, the one constructed by Bayesian Network corresponds to Biology's reality most. So we choose Bayesian Network to construct yeast gene regulatory network, of which process involves Bayesian Network structure learning and parameter learning. We gather and integrate the yeast data, by which we determine the network nodes. We learn the structure using MMHC algorithm, and use the simulated annealing algorithm to rating search so that we can get the best solution. Finally we use Bayesian algorithm or MLE algorithm to learn and determine the parameter. With the help of this regulatory network, researchers are able to have more acquaintance with the complex regulatory relationships in the gene regulatory network.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 3980-3985
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.3980
• Online since: November 2011
• Authors: Mei Chen Zhou, Fei Zhu
• Keywords: Bayesian Network (BN), Gene Regulatory Network, MMHC Algorithm, Yeast

References

[1] F. Yavari, F. Towhidkhah, SH. Gharibzadeh, Gene Regulatory Network Modeling using Bayesian Networks and Cross Correlation, Biomedical Engineering Conference, 2008. CIBEC 2008. Cairo International, 18-20 Dec. 2008, pp.1-4.

DOI: 10.1109/cibec.2008.4786041

[2] S. Zainudin, S. Deris, Combining Clustering and Bayesian Network for Gene Network Inference", Intelligent Systems Design and Applications, 2008. ISDA, 08. Eighth International Conference on, 26-28 Nov. 2008, pp.557-563.

DOI: 10.1109/isda.2008.183

[3] Baikang Pei, Rowe, D.W., Dong-Guk Shin, Reverse Engineering of Gene Regulatory Network by Integration of Prior Global Gene Regulatory Information", Bioinformatics and Biomedicine, 2008. BIBM, 08. IEEE International Conference on, 3-5 Nov. 2008, pp.129-134.

DOI: 10.1109/bibm.2008.55

[4] R. Pal, A. Datta, E.R. Dougherty, Bayesian Robustness in the Control of Gene Regulatory Networks", Statistical Signal Processing, 2007. SSP, 07. IEEE/SP 14th Workshop on, 26-29 Aug. 2007, pp.31-35, doi: 10. 1109/TSP. 2009. 2022872.

DOI: 10.1109/ssp.2007.4301212

[5] Higashigaki Tomoya, Kojima Kaname, Yamaguchi Rui, Inoue Masato, Imoto Seiya, Miyano Satoru, Identifying Hidden Confounders in Gene Networks by Bayesian Networks, BioInformatics and BioEngineering(BIBE), 2010 IEEE International Conference on, May 31 2010-June 3 2010, pp.168-173.

DOI: 10.1109/bibe.2010.35

[6] Rui Xu, D.C. Wunsch, R.L. Frank, Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, Oct. -Dec. 2007, pp.681-692.

DOI: 10.1109/tcbb.2007.1057

[7] A. Shermin, M.A. Orgun, A 2-Stage Approach for Inferring Gene Regulatory Networks Using Dynamic Bayesian Networks", Bioinformatics and Biomedicine, 2009. BIBM, 09. IEEE International Conference on, 1-4 Nov. 2009, pp.166-169.

DOI: 10.1109/bibm.2009.87

[8] P. Sarder,  W. Schierding,  J.P. Cobb,  A. Nehorai, Estimating Sparse Gene Regulatory Networks Using a Bayesian Linear Regression, NanoBioscience, IEEE Transactions on, June 2010, pp.121-131, doi: 10. 1109/TNB. 2010. 2043444.

DOI: 10.1109/tnb.2010.2043444

[9] Z.M. Ibrahim, A.Y. Tawfik, A. Ngom, Qualitative Motif Detection in Gene Regulatory Networks", Bioinformatics and Biomedicine, 2009. BIBM, 09. IEEE International Conference on, 1-4 Nov. 2009, pp.124-129, doi: 10. 1190/BIBM. 2009. 80.

DOI: 10.1109/bibm.2009.80

[10] K. Numata, S. Imoto, S. Miyano, Based Bayesian Network Learning Algorithm for Estimating Gene Neetworks", Bioinformatics and Biomedicine, 2008. BIBM, 08. IEEE International Conference on, 3-5 Nov. 2008, pp.357-360, doi: 10. 1109/BIBM. 2008. 85.

DOI: 10.1109/bibm.2008.85

[11] Sungmin Jung, Gyubok Moon, Yongjun Kim, Kyungwhan Oh, Planning based on Dynamic Bayesian Network algorithm using dynamic programming and variable elimination, Autonomous Robots and Agents, 2009. ICARA 2009. 4th International Conference on, 10-12 Feb. 2009, pp.109-114.

DOI: 10.1109/icara.2000.4803924

[12] K. Numata, S. Imoto, S. Miyano, Partial Order-Based Bayesian Network Learning Algorithm for Estimating Gene Networks", Bioinformarics and Biomedicine, 2008. BIBM, 08. IEEE International Conference on, 3-5 Nov. 2008, pp.357-360.

DOI: 10.1109/bibm.2008.85

[13] K. Numata, S. Imoto, S. Miyano, A Structure Learning Algorithm for Inference of Gene Networks from Microarray Gene Expression Data Using Bayesian Networks, Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on, 14-17 Oct. 2007, pp.1280-1284.

DOI: 10.1109/bibe.2007.4375731

[14] Nasimul Noman, Hitoshi Iba, Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics, Computational Biology and Bioinp\formatics IEEE/ACM Transactions on, Oct. -Dec. 2007, pp.634-647.

DOI: 10.1109/tcbb.2007.1058

[15] Yasuko Namatame, Maomi Ueno, Evaluating Learners Knowledge-structure using Bayesian networks, Advanced Learning Technologies, 2007. ICALT 2007. Seventh IEEE Internatioanl Conference on, pp.439-441, doi: 10. 1109/ICALT. 2007. 140.

DOI: 10.1109/icalt.2007.140