Predicting the effect of missense mutations on protein function: analysis with Bayesian networks

Background: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction. Results: Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a na

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Chris J. Needham, James R. Bradford, Andrew J. Bulpitt, Matthew A. Care, David R. Westhead
• Keywords: Bayesian Network, BMCBI 2006, Missense Mutation, Model Structures