Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm  

作　　者：Gui-xia Liu, Wei Feng, Han Wang, Lei Liu, Chun-guang ZhouCollege of Computer Science and Technology, Jilin University, Changchun 130012,P.R. China 

出　　处：《Journal of Bionic Engineering》2009年第1期86-92,共7页仿生工程学报（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 60433020, 60175024 and 60773095);European Commission under grant No. TH/Asia Link/010 (111084);the Key Science-Technology Project of the National Education Ministry of China (Grant No. 02090),and the Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, P. R. China

摘　　要：In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

关 键 词：gene regulatory networks two-stage learning algorithm Bayesian network immune evolutionary algorithm 

分 类 号：Q78[生物学—分子生物学]