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

Assessing performance of pathogenicity predictors using clinically relevant variant datasets.

Journal of medical genetics - 1 Aug 2021

Gunning Adam C, Fryer Verity, Fasham James, Crosby Andrew H, Ellard Sian, Baple Emma L, Wright Caroline F

Abstract excerpt

BACKGROUND: Pathogenicity predictors are integral to genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically relevant dataset has not been undertaken. METHODS: We derive two validation datasets: an 'open' dataset containing variants extracted from publicly available databases, similar to those commonly applied in previous benchmarking...

Read the complete abstract on PubMed

Topics

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

Share to Computational Biology
Share to Exome

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Variation

Share to Genomics

Share to ROC Curve

Source

Open in PubMed Open full text

Identifiers

PMID: 32843488
DOI: 10.1136/jmedgenet-2020-107003
PMCID: PMC8327323

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Assessing performance of pathogenicity predictors using clinically relevant variant datasets.PMID 32843488Read ↗

Assessing performance of pathogenicity predictors using clinically-relevant variant datasetsSemantic proximity Read ↗

Comparing Sequence-Based and Literature-Based Pathogenicity Scoring Methods for Human Variants.Semantic proximity Read ↗

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

Incorporation of clinical and molecular variant properties improves the performance of in silico pathogenicity prediction tools.Semantic proximity Read ↗

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

Benchmarking of variant pathogenicity prediction methods using a population genetics approachSemantic proximity Read ↗

Assessment of 13 in silico pathogenicity methods on cancer-related variants.Semantic proximity Read ↗

Insights for variant clinical interpretation based on a benchmark of 65 variant effect predictors.Semantic proximity Read ↗

SNPred outperforms other ensemble-based SNV pathogenicity predictors and elucidates the challenges of using ClinVar for evaluation of variant classification qualitySemantic proximity Read ↗

Assessment of variant effect predictors unveils variants difficulty as a critical performance indicatorSemantic proximity Read ↗

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

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

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