Analysis of an optimal hidden Markov model for secondary structure prediction.

ABSTRACT:

Background

Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models.

Results

Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model alpha-helices, 12 that model coil a

SUBMITTER: Martin J 

PROVIDER: S-EPMC1769381 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature