AlphaMissense Predictions and ClinVar Annotations: A Deep Learning Approach to Uveal Melanoma.

ABSTRACT:

Objective

Uveal melanoma (UM) poses significant diagnostic and prognostic challenges due to its variable genetic landscape. We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM.

Design

A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases.

Subjects

Genetic data from patients diagnosed with UM were analyzed, explicitly focusing on missense mutations sourced from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

Methods

We identified missense mutations frequently observed in UM using the COSMIC database, assessed their potential pathogenicity using AlphaMissense, and visualized mutations using AlphaFold. Clinical significance was cross-validated with entri

SUBMITTER: Taylor Gonzalez DJ 

PROVIDER: S-EPMC11925568 | biostudies-literature | 2025 May-Jun

REPOSITORIES: biostudies-literature