Article

An augmented transformer model trained on family specific variant data leads to improved prediction of variants of uncertain significance

2023-12-12

Abstract excerpt

<title>Abstract</title> <p> Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated to a disease, thus posing a challenge in the interpretation of genetic testing results. In this work, we present an improved gene specific approach to variant prediction that leverages a pre-trained protein language model for predicting VUS. Our deep learning model co...

Topics

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

Identifiers and source

Literature Corpus work: 2a4771c5-3a26-5560-996c-2ae49bd45f17
DOI: 10.21203/rs.3.rs-3705036/v2

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

An augmented transformer model trained on family specific variant data leads to improved prediction of variants of uncertain significanceDOI 10.21203/rs.3.rs-3705036/v2Read

VUS. Life: Leveraging Vector Embeddings for Rapid and Accurate Pathogenicity Prediction of Genetic VariantsSemantic proximity Read

Benchmarking DNA Foundation Models for zero-shot variant effect prediction: the role of context, training, and architectureSemantic proximity Read

Semantic embedding of variant effect annotations enables rapid and accurate pathogenicity prediction with VUS.Life.Semantic proximity Read

E-SNPs&GO: Embedding of protein sequence and function improves the annotation of human pathogenic variantsSemantic proximity Read

Learning sequence to predict gain- or loss-of-function variantsSemantic proximity Read

Interpretable variant effect prediction from genomic foundation model representationsSemantic proximity Read

Deep structured learning realizes variant prioritization for Mendelian diseasesSemantic proximity Read

Benchmarking DNA Foundation Models for zero-shot variant effect prediction: the role of context, training, and architectureSemantic proximity Read

Predicting Genetic Variant Pathogenicity Using Vector EmbeddingsSemantic proximity Read

Protein Language Models Rescue Variant Pathogenicity Prediction in Intrinsically Disordered Regions Through Synergistic Integration with Structure-Based MethodsSemantic proximity Read

The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human GenomicsSemantic proximity Read

DeNovoSeer:a deep learning framework for pathogenicity prediction of De novo mutations.Semantic proximity Read