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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Autores principales: Betel, Doron, Koppal, Anjali, Agius, Phaedra, Sander, Chris, Leslie , Christina
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Method
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
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author Betel, Doron
Koppal, Anjali
Agius, Phaedra
Sander, Chris
Leslie , Christina
author_facet Betel, Doron
Koppal, Anjali
Agius, Phaedra
Sander, Chris
Leslie , Christina
author_sort Betel, Doron
collection PubMed
description 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.
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spelling pubmed-29457922010-10-07 Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites Betel, Doron Koppal, Anjali Agius, Phaedra Sander, Chris Leslie , Christina Genome Biol Method 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. BioMed Central 2010 2010-08-27 /pmc/articles/PMC2945792/ /pubmed/20799968 http://dx.doi.org/10.1186/gb-2010-11-8-r90 Text en Copyright ©2010 Betel et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Betel, Doron
Koppal, Anjali
Agius, Phaedra
Sander, Chris
Leslie , Christina
Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
title Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
title_full Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
title_fullStr Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
title_full_unstemmed Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
title_short Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
title_sort comprehensive modeling of microrna targets predicts functional non-conserved and non-canonical sites
topic Method
url 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
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