Article

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

2019-08-19

Abstract excerpt

A rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 5778fad5-7dc2-5ac1-ae2a-f31290240f1a
DOI: 10.1101/739474

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

SVFX: a machine-learning framework to quantify the pathogenicity of structural variantsDOI 10.1101/739474Read

StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variantsSemantic proximity Read

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

SyMetrics: an integrated machine learning model for evaluating the pathogenicity of synonymous variants in the human genome.Semantic proximity Read

SVScore: An Impact Prediction Tool For Structural VariationSemantic proximity Read

MVP: predicting pathogenicity of missense variants by deep learningSemantic proximity Read

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.Semantic proximity Read

SyMetrics: An Integrated Machine Learning Model for Evaluating the Pathogenicity of Synonymous Variants in the Human GenomeSemantic proximity Read

SVScore: an impact prediction tool for structural variation.Semantic proximity Read

MARiO: predicting cancer variant pathogenicity by integrating <i>in silico</i> evaluation and patient-level mutational contextsSemantic proximity Read

PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant SplicingSemantic proximity Read

PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing.Semantic proximity Read

Mining Thousands of Genomes to Classify Somatic and Pathogenic Structural VariantsSemantic proximity Read