MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somat...

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Detalles Bibliográficos
Autores principales: Mort, Matthew, Sterne-Weiler, Timothy, Li, Biao, Ball, Edward V, Cooper, David N, Radivojac, Predrag, Sanford, Jeremy R, Mooney, Sean D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054890/
https://www.ncbi.nlm.nih.gov/pubmed/24451234
http://dx.doi.org/10.1186/gb-2014-15-1-r19

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054890/
https://www.ncbi.nlm.nih.gov/pubmed/24451234
http://dx.doi.org/10.1186/gb-2014-15-1-r19

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