An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer.

ABSTRACT:

Background and objective

Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments. This inconsistency is evident even for well-established cancer driver mutations. This study aims to develop an ensemble machine learning approach to evaluate the performance (rank) of pathogenic and conservation scoring algorithms (PCSAs) based on their ability to distinguish pathogenic driver mutations from benign passenger (non-driver) mutations in head and neck squamous cell carcinoma (HNSC).

Methods

The study used a dataset from 502 HNSC patients, classifying mutations based on 299

SUBMITTER: Das S 

PROVIDER: S-EPMC11744934 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature