DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.

ABSTRACT:

Background

A significant challenge in precision medicine is confidently identifying mutations detected in sequencing processes that play roles in disease treatment or diagnosis. Furthermore, the lack of representativeness of single nucleotide variants in public databases and low sequencing rates in underrepresented populations pose defies, with many pathogenic mutations still awaiting discovery. Mutational pathogenicity predictors have gained relevance as supportive tools in medical decision-making. However, significant disagreement among different tools regarding pathogenicity identification is rooted, necessitating manual verification to confirm mutation effects accurately.

Results

This article presents a cross-platform mobile application, DTreePred, an online visualizatio

SUBMITTER: Gomes DHF 

PROVIDER: S-EPMC11983909 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature