Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

BACKGROUND: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or th...

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Detalles Bibliográficos
Autores principales: Peto, Myron, Kloczkowski, Andrzej, Honavar, Vasant, Jernigan, Robert L
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655094/
https://www.ncbi.nlm.nih.gov/pubmed/19014713
http://dx.doi.org/10.1186/1471-2105-9-487

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655094/
https://www.ncbi.nlm.nih.gov/pubmed/19014713
http://dx.doi.org/10.1186/1471-2105-9-487

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