Article

An interpretable molecular framework for predicting cancer driver missense mutations

2025-09-08

Abstract excerpt

Missense mutations play a critical role in human disease, contributing to both inherited disorders and cancer. However, accurately predicting their functional impact, particularly for cancer driver mutations, remains a major challenge due to limited validated labels and the complex molecular basis of oncogenesis. Here, we systematically characterized over 120,000 missense variants across pathogenic, benign, driver...

Topics

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

Identifiers and source

Literature Corpus work: 89924b15-f9fa-5635-a49d-ee629db1281e
DOI: 10.1101/2025.09.03.674130

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

An interpretable molecular framework for predicting cancer driver missense mutationsDOI 10.1101/2025.09.03.674130Read

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

CHASMplus reveals the scope of somatic missense mutations driving human cancersSemantic proximity Read

Leveraging cancer mutation data to predict the pathogenicity of germline missense variantsSemantic proximity Read

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

PON-P: integrated predictor for pathogenicity of missense variants.Semantic proximity Read

MARiO: predicting cancer variant pathogenicity by integrating <i>in silico</i> evaluation and patient-level mutational contextsSemantic proximity Read

Comprehensive assessment of computational algorithms in predicting cancer driver mutations.Semantic proximity Read

Characterizing and comparing missense variants in monogenic disease and in cancerSemantic proximity Read

Are Next-Generation Pathogenicity Predictors Applicable to Cancer?Semantic proximity Read

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

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

Are the Next-Generation Pathogenicity Predictors Applicable to Cancer?Semantic proximity Read