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Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks
Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by...
| Autores principales: | , , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2008
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570248/ https://www.ncbi.nlm.nih.gov/pubmed/18989393 http://dx.doi.org/10.1371/journal.pcbi.1000213 |