Using Bayesian networks to discover relations between genes, environment, and disease


We review the applicability of Bayesian networks (BNs) for discovering relations between genes, environment, and disease. By translating probabilistic dependencies among variables into graphical models and vice versa, BNs provide a comprehensible and modular framework for representing complex systems.

We first describe the Bayesian network approach and its applicability to understanding the genetic and environmental basis of disease. We then describe a variety of algorithms for learning the structure of a network from observational data.

Because of their relevance to real-world applications, the topics of missing data and causal interpretation are emphasized. The BN approach is then exemplified through application to data from a population-based study of bladder cancer in New Hampshire, USA.

For didactical purposes, we intentionally keep this example simple. When applied to complete data records, we find only minor differences in the performance and results of different algorithms.

Subsequent incorporation of partial records through application of the EM algorithm gives us greater power to detect relations. Allowing for network structures that depart from a strict causal interpretation also enhances our ability to discover complex associations including gene-gene (epistasis) and gene-environment interactions.

While BNs are already powerful tools for the genetic dissection of disease and generation of prognostic models, there remain some conceptual and computational challenges. These include the proper handling of continuous variables and unmeasured factors, the explicit incorporation of prior knowledge, and the evaluation and communication of the robustness of substantive conclusions to alternative assumptions and data manifestations.

Author: Chengwei SuAngeline AndrewMargaret R KaragasMark E Borsuk
Credits/Source: BioData Mining 2013, 6:6



Published on: 2013-03-21



News Provider: 7thSpace Interactive

Copyright by the authors listed above - made available via BioMedCentral (Open Access). Please make sure to read our disclaimer prior to contacting 7thSpace Interactive. To contact our editors, visit our online helpdesk. If you wish submit your own press release, click here.

Social Bookmarking
RETWEET This! | Digg this! | Post to del.icio.us | Post to Furl | Add to Netscape | Add to Yahoo! | Rojo



Comments

There are no comments available. Be the first to write a comment.


You need to enable Javascript to post a comment.


Custom Search

Username
Password










© 2014 7thSpace Interactive
All Rights Reserved - About | Disclaimer | Helpdesk
There are currently 20282 people browsing 7thSpace (S2)