Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure

Background: Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian networks (DBNs) and support vector machines (SVMs) have been shown to provide state-of-the-art performance in secondary structure prediction. As the size of the protein database grows, it becomes feasible to use a richer model in an effort to capture subtle correlations among the amino acids and the predicted labels. In this context, it is beneficial to derive sparse models that discourage over-fitting and provide biological insight. Results: In this paper, we first show that we are able to obtain accurate secondary structure predictions. Our perresidue accuracy on a well established and difficult benchmark (CB513) is 80.3%, which is comparable to the stateof-the-art evaluated on this dataset. We then introduce an algorithm for sparsifying the parameters of a DBN. Using this algorithm, we can automat...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Zafer Aydin, Ajit Singh, Jeff Bilmes, William Stafford Noble
• Keywords: Biometrics, BMCBI 2011, Secondary Structure Elements, Secondary Structure Predictions, Support Vector Machines