|Title||Incorporating prior expert knowledge in learning Bayesian networks from genetic epidemiological data|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||C Su, ME Borsuk, A Andrew, and M Karagas|
|Conference Name||2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014|
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.