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...

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Detalles Bibliográficos
Autores principales: Reynolds, Sheila M., Käll, Lukas, Riffle, Michael E., Bilmes, Jeff A., Noble, William Stafford
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2008
Materias:
Research Article
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

Internet

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

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