SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants.

Abstract excerpt

Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to predict the pathogenicity of deletions, insertions and duplications structural variations that occur in exons. We constructed three types of annotation features for each...

Topics

Share this publication in a Topic to start or enrich a Post.

• Share to Exons
• Share to Machine Learning
• Share to Molecular Sequence Annotation