Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Performance of a Protein Language Model for Variant Annotation in Cardiac Disease.

Journal of the American Heart Association - 15 Oct 2024

Hochstadt Aviram, Barbhaiya Chirag, Aizer Anthony, Bernstein Scott, Cerrone Marina, Garber Leonid, Holmes Douglas, Knotts Robert J, Kushnir Alex, Martin Jacob, Park David, Spinelli Michael, Yang Felix, Chinitz Larry A, Jankelson Lior

Abstract excerpt

BACKGROUND: Genetic testing is a cornerstone in the assessment of many cardiac diseases. However, variants are frequently classified as variants of unknown significance, limiting the utility of testing. Recently, the DeepMind group (Google) developed AlphaMissense, a unique artificial intelligence-based model, based on language model principles, for the prediction of missense variant pathogenicity. We aimed to...

Read the complete abstract on PubMed

Topics

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

Share to Artificial Intelligence
Share to Databases, Genetic

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to Genetic Testing

Share to Genetic Variation

Share to Heart Diseases

Share to Molecular Sequence Annotation

Share to Mutation, Missense

Source

Open in PubMed Open full text

Identifiers

PMID: 39392163
DOI: 10.1161/JAHA.124.036921
PMCID: PMC11935564

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 10 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Performance of a Protein Language Model for Variant Annotation in Cardiac Disease.PMID 39392163Read ↗

Performance of a Protein Language Model for Variant Annotation in Cardiac DiseaseSemantic proximity Read ↗

CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretationSemantic proximity Read ↗

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.Semantic proximity Read ↗

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditionsSemantic proximity Read ↗

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditionsSemantic proximity Read ↗

Gene-specific artificial intelligence-based variant classification engine: results of a time-capsule experimentSemantic proximity Read ↗

Making Sense of Missense: Benchmarking MutScore for Variant Interpretation in Inherited Cardiac Diseases.Semantic proximity Read ↗

A disease-specific language model for variant pathogenicity in cardiac and regulatory genomicsSemantic proximity Read ↗

Variant annotation across homologous proteins (“Paralogue Annotation”) identifies disease-causing missense variants with high precision, and is widely applicable across protein familiesSemantic proximity Read ↗

Impact of variant reclassification in the clinical setting of cardiovascular genetics.Semantic proximity Read ↗

DiscoVari: A Web-Based Precision Medicine Tool for Predicting Variant Pathogenicity in Cardiomyopathy- and Channelopathy-Associated Genes.Semantic proximity Read ↗

Clinical Utility of Protein Language Models in Resolution of Variants of Uncertain Significance in KCNQ1, KCNH2, and SCN5A Compared With Patch-Clamp Functional Characterization.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.