Article

AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity prediction

2026-05-04

Abstract excerpt

Missense variant interpretation remains challenging because pathogenicity depends on heterogeneous evidence from population frequency, evolutionary conservation, transcript context, amino acid substitution severity, prior pathogenicity predictors and protein-language-model-derived features. We present AnnotateMissense, a scalable annotation, benchmarking and genome-wide prediction framework for missense variant in...

Topics

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

Identifiers and source

Literature Corpus work: 6eae8089-eff1-58ce-b392-7fa597436f36
DOI: 10.64898/2026.05.03.722489

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity predictionDOI 10.64898/2026.05.03.722489Read

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

A New Era in Missense Variant Analysis: Statistical Insights and the Introduction of VAMPP-Score for Pathogenicity AssessmentSemantic proximity Read

MissenseHMM: state-based annotations for missense variants through joint modeling of pathogenicity scoresSemantic proximity Read

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

Genetic variant pathogenicity prediction trained using disease-specific clinical sequencing datasetsSemantic proximity Read

Improved pathogenicity prediction for rare human missense variants.Semantic proximity Read

Assessing the performance of 28 pathogenicity prediction methods on rare single nucleotide variants in coding regions.Semantic proximity Read

A fine-tuned genomic language model captures nucleotide-level information overlooked by missense variant impact predictorsSemantic proximity Read

ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants.Semantic proximity Read

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.Semantic proximity Read

Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges.Semantic proximity Read

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