By Topic

Inferring Genetic Regulatory Networks with an Hierarchical Bayesian Model and a Parallel Sampling Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Mariana Recamonde Mendoza ; Inst. de Inf., Univ. Fed. do Rio Grande do Sul UFRGS, Porto Alegre, Brazil ; Adriano Velasque Werhli

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.

Published in:

2010 Eleventh Brazilian Symposium on Neural Networks

Date of Conference:

23-28 Oct. 2010