Analysis of an optimal hidden Markov model for secondary structure prediction |
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Authors: | Juliette Martin Jean-François Gibrat François Rodolphe |
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Institution: | 1. INSERM U726, Equipe de Bioinformatique Génomique et Moléculaire Université Denis Diderot Paris 7, 2 place jussieu, 75251, Paris, Cedex 05, France 2. INRA, Unité Mathématiques Informatique et Génome, Domaine de Vilvert, 78352, Jouy en Josas Cedex, France
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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. |
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Keywords: | |
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