Article

Machine learning approach to assess the pathogenicity of BRCA1/2 genetic variants : brca-NOVUS

2023-10-20

Abstract excerpt

<h4>ABSTRACT</h4> Breast cancer is globally the leading type of cancer in terms of both incidence and mortality. BRCA1 and BRCA2 gene variants have long been linked to and studied in context of the disease. Rapid variant discovery has further been made freely accessible by advances in Next-generation sequencing, making it a demanding task to accurately interpret these variants for clinical and research applicatio...

Topics

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

Identifiers and source

Literature Corpus work: ab47b6d4-14db-5182-beef-11d68bec4f4e
DOI: 10.1101/2023.10.20.23297295

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning approach to assess the pathogenicity of BRCA1/2 genetic variants : brca-NOVUSDOI 10.1101/2023.10.20.23297295Read

Cancer genomic profiling predicts pathogenicity of BRCA1 and BRCA2 variantsSemantic proximity Read

Predicting Ovarian/Breast Cancer Pathogenic Risks of Human BRCA1 Gene Variants of Unknown Significance.Semantic proximity Read

BRCAIndica: a resource for ACMG/AMP classified BRCA1 and BRCA2 variants.Semantic proximity Read

Understanding and predicting the functional consequences of missense mutations in BRCA1 and BRCA2.Semantic proximity Read

Real-world evaluation of deep learning algorithms to classify functional pathogenic germline variantsSemantic proximity Read

BRCAIndica: a Resource for ACMG/AMP classified BRCA1 and BRCA2 VariantsSemantic proximity Read

Predicting ovarian/breast cancer pathogenic risks of BRCA1 gene variants of unknown significanceSemantic proximity Read

BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy.Semantic proximity Read

Germline Missense Variants in BRCA1: New Trends and Challenges for Clinical AnnotationSemantic proximity Read

Clinical implications of VUS reclassification in a single-centre series from application of ACMG/AMP classification rules specified for BRCA1/2.Semantic proximity Read

MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model.Semantic proximity Read

Integration of functional assay data results provides strong evidence for classification of hundreds of BRCA1 variants of uncertain significance.Semantic proximity Read