Paper Info
Title
Inferring Genetic Regulatory Networks with an Hierarchical Bayesian Model and a Parallel Sampling Algorithm
Abstract
Bayesian Networks (BNs) are used in a wide range of applications, being the representation of regulatory networks a recurrent one. Nowadays great interest is dedicated to the problem of inferring the network's structure solely from the data. Aiming more precise results, the inclusion of extra knowledge in the inference process has been already suggested, as well as a Bayesian coupling scheme for learning genetic regulatory networks from a combination of related data sets which were obtained under different experimental conditions and are therefore potentially associated with different active sub-pathways. Furthermore, this approach has been combined to a MCMC sampling scheme and it has been verified that due to the complexity of the model, the MCMC suffered from poor convergence. We now propose the use of a Metropolis Coupled Markov Chain Monte Carlo (MC)^3 algorithm in order to improve the mixing and convergence of the inference process.
Year
DOI
Venue
2010
10.1109/SBRN.2010.24
Neural Networks
Keywords
Field
DocType
Markov processes,Monte Carlo methods,belief networks,biology computing,genetics,inference mechanisms,parallel algorithms,Bayesian coupling scheme,Bayesian networks,MCMC sampling scheme,genetic regulatory networks,hierarchical Bayesian model,inference process,metropolis coupled Markov Chain Monte Carlo algorithm,parallel sampling algorithm,Bayesian Hierarchical Model,Bayesian Networks,Genetic Regulatory Networks,MC3
Markov process,Bayesian inference,Markov chain Monte Carlo,Inference,Computer science,Parallel algorithm,Algorithm,Bayesian network,Bayesian hierarchical modeling,Artificial intelligence,Machine learning,Bayesian probability
Conference
ISSN
ISBN
Citations 
1522-4899 E-ISBN : 978-0-7695-4210-2
978-0-7695-4210-2
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Mariana Recamonde Mendoza100.34
Adriano Velasque Werhli243.86