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

Pathogenicity Prediction of Missense Variations in Hereditary Cancer Genes.

International journal of molecular sciences - 7 Mar 2026

Akyerli Cemaliye B, Gerdan Gizel, Bülbül Alper, Keskin-Karakoyun Hilal, Yüksel Şirin K, Timucin Emel

Abstract excerpt

HerCanPred, a machine-learning-based pathogenicity classifier specifically optimized for 63 cancer-predisposition genes, was developed to improve the interpretation of missense variants in hereditary cancer syndromes. This model integrates sequence conservation with structural features derived from AlphaFold2 (AF2) structures. HerCanPred achieved a strong performance, outperforming 23 established predictors. SHAP...

Read the complete abstract on PubMed

Topics

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

Share to Conserved Sequence
Share to Genes, Neoplasm

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Mutation, Missense

Share to Neoplasms

Share to Protein Folding

Share to Protein Interaction Maps

Share to Uncertainty

Source

Open in PubMed Open full text

Identifiers

PMID: 41828669
DOI: 10.3390/ijms27052453
PMCID: PMC12985416

Genes

FH
MLH1
NF1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Pathogenicity Prediction of Missense Variations in Hereditary Cancer Genes.PMID 41828669Read ↗

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

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

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

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

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic featuresSemantic proximity Read ↗

Human Proteome-wide Mechanistic Interpretation of Missense Variants through Protein Feature Enrichment ScoreSemantic proximity Read ↗

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification.Semantic proximity Read ↗

SIGMA leverages protein structural information to predict the pathogenicity of missense variants.Semantic proximity Read ↗

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

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

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

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.Semantic proximity Read ↗

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