title: A full bayesian approach for boolean genetic network inference.
abstract: Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
authors.forename: Shengtong
authors.initials: S
authors.lastname: Han
authors.affiliation: Department of Statistics, Iowa State University, Ames, IA, United States of America.
authors.forename: Raymond K W
authors.initials: RK
authors.lastname: Wong
authors.affiliation: Department of Statistics, University of California Davis, Davis, CA, United States of America.
authors.forename: Thomas C M
authors.initials: TC
authors.lastname: Lee
authors.affiliation: Department of Information Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
authors.forename: Linghao
authors.initials: L
authors.lastname: Shen
authors.affiliation: Department of Information Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China; University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Shuo-Yen R
authors.initials: SY
authors.lastname: Li
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
authors.forename: Xiaodan
authors.initials: X
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002453:Cell Cycle / Q000235:genetics; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method