Article

AlphaRING-X: accurate interpretation of missense variant deleteriousness based on protein structural stability

2024-11-15

Abstract excerpt

<h4>Background</h4> Accurate interpretation of missense variants remains a significant challenge hindering genomic diagnosis. While state-of-the-art machine learning and deep learning predictors offer high accuracy, they often lack the transparency required for clinical evidence weighting. To bridge this gap between accuracy and clinical utility, we developed AlphaRING-X. <h4>Methods</h4> AlphaRING-X is an open-...

Topics

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

Identifiers and source

Literature Corpus work: 3123a053-1860-55f8-a7e5-deec333cec57
DOI: 10.1101/2024.11.12.623182

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

AlphaRING-X: accurate interpretation of missense variant deleteriousness based on protein structural stabilityDOI 10.1101/2024.11.12.623182Read

Toward Advanced Quantitative Prediction of Missense Variant EffectsSemantic proximity Read

StabilitySort: assessment of protein stability changes on a genome-wide scale to prioritize potentially pathogenic genetic variation.Semantic proximity Read

Prediction and interpretation of deleterious coding variants in terms of protein structural stabilitySemantic proximity Read

Predicting the pathogenicity of missense variants using features derived from AlphaFold2Semantic proximity Read

Prediction and interpretation of deleterious coding variants in terms of protein structural stability.Semantic proximity Read

QAFI: A Novel Method for Quantitative Estimation of Missense Variant Impact Using Protein-Specific Predictors and Ensemble LearningSemantic proximity Read

Predicting the pathogenicity of missense variants using features derived from AlphaFold2.Semantic proximity Read

AFFIPred: AlphaFold2 Structure-based Functional Impact Prediction of Missense VariationsSemantic proximity Read

DAVE: how to use explainable AI to interpret missense variants for genome diagnostics based on functional protein modelingSemantic proximity Read

MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.Semantic proximity Read

SNPeffect 5.0: large-scale structural phenotyping of protein coding variants extracted from next-generation sequencing data using AlphaFold models.Semantic proximity Read

Accurate Identification and Mechanistic Evaluation of Pathogenic Missense Variants with <i>Rhapsody-2</i>Semantic proximity Read