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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...
| Autores principales: | , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2008
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| Materias: | |
| 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 |