INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome.

ABSTRACT: Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied problem. Here, we developed INDELpred, a machine-learning-based predictive model for discerning pathogenic from benign indels. INDELpred was established based on key features, including allele frequency, indel length, function-based features, and gene-based features. A set of comprehensive evaluation analyses demonstrated that INDELpred exhibited superior performance over competing methods in terms of computational efficiency and prediction accuracy. Importantly, INDELpred highlighted the crucial role of function-based features in identifying pathoge

SUBMITTER: Wei Y 

PROVIDER: S-EPMC11321314 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature