A New Method to Build Gene Regulation Network Based on Fuzzy Hierarchical Clustering Methods

Maghsoodi, Masoume (2016): A New Method to Build Gene Regulation Network Based on Fuzzy Hierarchical Clustering Methods. Published in: International Academic Journal of Science and Engineering , Vol. 3, No. 6 (June 2016): pp. 169-176.

Abstract

The construction of genetic regulatory networks is understanding the relationship among genes or circuits which regulate the conditions of cells in response to internal or external stimuli. In fact, the objective is to understand the network of relationship among genes which determine which genes are responsible for activating other genes. The understanding of relationships may help to identify the genes which are involved in a disease and design the drugs. The most important limitations in gene regulatory network inference are low number of samples, noise penetration possibility, and large number of genes. There are different models to build gene regulatory network. This study used fuzzy hierarchical clustering method to infer gene regulatory network. Using clustering, the similar genes will be in a cluster. Many edges therefore will be removed. The final assessments showed that the genes clustering increased the efficiency of gene regulation network inference methods.

• Item Type: MPRA Paper
• Language: English
• Keywords: Principal Component Analysis, Head Cluster, Clustering, Gene Regulation Network.
• Subjects: C - Mathematical and Quantitative Methods > C0 - General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling

References

DeRisi, Joseph L., Vishwanath R. Iyer, and Patrick O. Brown. "Exploring the metabolic and genetic control of gene expression on a genomic scale." Science 278.5338 (1997): 680-686.

Schlitt, Thomas, and Alvis Brazma. "Current approaches to gene regulatory network modelling." BMC bioinformatics 8.6 (2007): S9.

Huang, Xun, and Zhike Zi. "Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR)." Molecular BioSystems 10.8 (2014): 2023-2030.

Meyer, Patrick, Daniel Marbach, Sushmita Roy, and Manolis Kellis. "Information-Theoretic Inference of Gene Networks Using Backward Elimination." In Biocomp, pp. 700-705. 2010.

Irrthum, Alexandre, Louis Wehenkel, and Pierre Geurts. "Inferring regulatory networks from expression data using tree-based methods." PloS one 5.9 (2010): e12776.

Abbasi, Ehsan, Mahjoob, Mohammad and Yazdanpanah, Reza, 2013, September. Controlling of Quadrotor UAV Using a Fuzzy System for Tuning the PID Gains in Hovering Mode. In 10th Int. Conf. Adv. Comput. Entertain. Technol (pp. 1-6).

Yazdanpanah, Reza, Mahjoob J., Mohammad and Abbasi, Ehsan, 2013. Fuzzy LQR controller for heading control of an unmanned surface vessel. In International Conference in Electrical and Electronics Engineering (pp. 73-78).

Faith, J.J., Hayete, B., Thaden, J.T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J.J. and Gardner, T.S., 2007. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS biol, 5(1), p.e8.

Sinaee, Mehrnoosh, and Eghbal G. Mansoori. "Fuzzy rule based clustering for gene expression data." Intelligent Systems Modelling & Simulation (ISMS), 2013 4th International Conference on. IEEE, 2013.

Shlens, Jonathon. "A tutorial on principal component analysis." arXiv preprint arXiv:1404.1100 (2014).

The DREAM project. http://wiki.c2b2.columbia.edu/dream/index.php/The_DREAM_Project. 2008 ,pp. 1–7.