A novel seven-tier framework for the classification of MEFV missense variants using adaptive and rigid classifiers.

ABSTRACT: There is a great discrepancy between the clinical categorization of MEFV gene variants and in silico tool predictions. In this study, we developed a seven-tier classification system for MEFV missense variants of unknown significance and recommended a generalized pipeline for other gene classifications. We extracted 12,017 human MEFV gene variants from the Ensembl database. After extraction, we detected 6034 missense variants. In the next step, we selected 42 in silico tools for our classification model. We determined the optimal value via the scores from three in silico tools. For the implementation of machine learning methods, we used two bagging methods and two boosting methods. After predicting known variants, we applied our model to 5507 variants of unknown significance. In the final s

SUBMITTER: Alay MT 

PROVIDER: S-EPMC11911402 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature