SVFX: a machine learning framework to quantify the pathogenicity of structural variants

There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity scores to somatic and germline SVs. In particular, we g...

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Detalles Bibliográficos
Autores principales: Kumar, Sushant, Harmanci, Arif, Vytheeswaran, Jagath, Gerstein, Mark B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7650198/
https://www.ncbi.nlm.nih.gov/pubmed/33168059
http://dx.doi.org/10.1186/s13059-020-02178-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7650198/
https://www.ncbi.nlm.nih.gov/pubmed/33168059
http://dx.doi.org/10.1186/s13059-020-02178-x

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