Incorporating prior expert knowledge in learning Bayesian networks from genetic epidemiological data

Published

Conference Paper

We consider the applicability of Bayesian networks (BNs) for discovering relations between genes, environment, and disease. Most state-of-the-art BN structure learning algorithms are not capable of learning structures from data containing missing values, which is a norm in genetic epidemiological data. In addition, there exists a wealth of existing prior knowledge which could be incorporated to improve computational efficiency in BN structure learning. To address these challenges, we applied a Markov chain Monte Carlo based BN structure learning algorithm to data from a population-based study of bladder cancer in New Hampshire, USA. A large improvement in computational efficiency is achieved under this approach. © 2014 IEEE.

Full Text

Duke Authors

Cited Authors

  • Su, C; Borsuk, ME; Andrew, A; Karagas, M

Published Date

  • January 1, 2014

Published In

  • 2014 Ieee Conference on Computational Intelligence in Bioinformatics and Computational Biology, Cibcb 2014

International Standard Book Number 13 (ISBN-13)

  • 9781479945368

Digital Object Identifier (DOI)

  • 10.1109/CIBCB.2014.6845507

Citation Source

  • Scopus