SVFX: a machine learning framework to quantify the pathogenicity of structural variants
Abstract 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 particu...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-11-01
|
| Series: | Genome Biology |
| Online Access: | http://link.springer.com/article/10.1186/s13059-020-02178-x |