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BMCBI
2011
9 years 3 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 ...
Zafer Aydin, Ajit Singh, Jeff Bilmes, William Staf...
ARTMED
2004
133views more  ARTMED 2004»
9 years 11 months ago
Bayesian network multi-classifiers for protein secondary structure prediction
Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-stru...
Víctor Robles, Pedro Larrañaga, Jos&...
BMCBI
2008
137views more  BMCBI 2008»
9 years 11 months ago
A dynamic Bayesian network approach to protein secondary structure prediction
Background: Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful informatio...
Xin-Qiu Yao, Huaiqiu Zhu, Zhen-Su She
BMCBI
2010
229views more  BMCBI 2010»
9 years 11 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
JMLR
2000
134views more  JMLR 2000»
9 years 11 months ago
Learning with Mixtures of Trees
This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu...
Marina Meila, Michael I. Jordan
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