Transmembrane helix prediction using amino acid property features and latent semantic analysis

BACKGROUND: Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often res...

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Detalles Bibliográficos
Autores principales: Ganapathiraju, Madhavi, Balakrishnan, N, Reddy, Raj, Klein-Seetharaman, Judith
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Proceedings
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2259405/
https://www.ncbi.nlm.nih.gov/pubmed/18315857
http://dx.doi.org/10.1186/1471-2105-9-S1-S4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2259405/
https://www.ncbi.nlm.nih.gov/pubmed/18315857
http://dx.doi.org/10.1186/1471-2105-9-S1-S4

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