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...

Descripción completa

Detalles Bibliográficos
Autores principales: Camps-Valls, Gustavo, Chalk, Alistair M, Serrano-López, Antonio J, Martín-Guerrero, José D, Sonnhammer, Erik LL
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Research Article
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

Internet

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

Ejemplares similares