Sciweavers

Share
BMCBI
2011

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

7 years 8 months ago
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...
Zafer Aydin, Ajit Singh, Jeff Bilmes, William Staf
Added 24 Aug 2011
Updated 24 Aug 2011
Type Journal
Year 2011
Where BMCBI
Authors Zafer Aydin, Ajit Singh, Jeff Bilmes, William Stafford Noble
Comments (0)
books