Comparison of missing data handling methods for variant pathogenicity predictors.

ABSTRACT: Modern clinical genetic tests utilize next-generation sequencing (NGS) approaches to comprehensively analyze genetic variants from patients. Out of millions of variants, clinically relevant variants that match the patient's phenotype must be identified accurately and rapidly. As manual evaluation is not a feasible option for meeting the speed and volume requirements of clinical genetic testing, automated solutions are needed. Various machine learning (ML), artificial intelligence (AI), and in silico variant pathogenicity predictors have been developed to solve this challenge. These solutions rely on comprehensive data and struggle with the sparse genetic annotations. Therefore, careful treatment of missing data is necessary, and the selected methods may have a huge impact on the acc

SUBMITTER: Sarkka M 

PROVIDER: S-EPMC12526054 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature