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Profiled support vector machines for antisense oligonucleotide efficacy prediction
BACKGROUND: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is cru...
| Autores principales: | , , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2004
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC526382/ https://www.ncbi.nlm.nih.gov/pubmed/15383156 http://dx.doi.org/10.1186/1471-2105-5-135 |