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

From Pathogenicity to Mechanism: A Variant Interpretation Framework for Monogenic Epilepsy.

Clinical genetics - 1 Oct 2026

Ye Shaopei, Chen Peng

Abstract excerpt

Pathogenicity predictors exceed AUROC 0.97 on expert-curated ClinVar, yet the monogenic-epilepsy variant-of-uncertain-significance backlog persists because pathogenicity alone provides no direction-of-effect, no scalable mechanism-to-treatment mapping, and no evidence that updates as ClinVar grows. SeizeVar couples a consensus pathogenicity head (random forest plus ESM-2 LoRA cross-attention) to a...

Read the complete abstract on PubMed

Topics

Humans
Epilepsy
Genetic Predisposition to Disease
Genetic Variation
Mutation

Source

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

Open in PubMed Open full text

Identifiers

PMID: 42384027
DOI: 10.1111/cge.70206
PMCID: PMC13533854

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 27 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

From Pathogenicity to Mechanism: A Variant Interpretation Framework for Monogenic Epilepsy.PMID 42384027Read ↗

AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity predictionSemantic proximity Read ↗

Critical assessment of missense variant effect predictors on disease-relevant variant dataSemantic proximity Read ↗

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

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

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

DITTO: An explainable machine-learning model for transcript- specific variant pathogenicity predictionSemantic proximity Read ↗

Annotating pathogenic non-coding variants in genic regions.Semantic proximity Read ↗

MVP predicts the pathogenicity of missense variants by deep learning.Semantic proximity Read ↗

EpiPred: A gene-specific machine learning model for classifying missense variants in the epilepsy-related gene <i>STXBP1</i>Semantic proximity Read ↗

Evaluating novel in silico tools for accurate pathogenicity classification in epilepsy-associated genetic missense variants.Semantic proximity Read ↗

Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease.Semantic proximity Read ↗

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

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