Inferring Gene Regulatory Networks, Using Machine Learning Approaches

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Gheiby, Sanaz | 2012

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 43503 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Manzuri, Mohammad Taghi
Abstract:
Gene regulatory network consists of a set of genes; interacting with each other via their protein products. Such interations lead to the regulation of the genes’ production rate. A breakdown in the regulatory process, may lead to some kinds of diseases. Therefore, understanding the gene regulatory process, is beneficial for both diagnosis and treatment. In this thesis, gene regulatory networks are modeled by the means of dynamic Bayesian networks. We have used sampling based methods, in order to learn the network structure. As these methos have a very high computational cost; we have used a correlation test to prune the search space. This way, an undirected network skeleton is obtained; for which we further estimate the edge directions by sampling methods. As the simulation results demonstrait, the proposed method has improved the recently proposed algorithms, in terms of speed and/or correctness.
Keywords:
Machine Learning ; Gene Regulation ; Dynamic Bayesian Network ; Gene Regulatory Networks