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Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other targe...

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Detalles Bibliográficos
Autores principales: Betel, Doron, Koppal, Anjali, Agius, Phaedra, Sander, Chris, Leslie , Christina
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2945792/
https://www.ncbi.nlm.nih.gov/pubmed/20799968
http://dx.doi.org/10.1186/gb-2010-11-8-r90
Descripción
Sumario:mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.