XGBMUT: Predicting the Functional Impact of Missense Mutations Using an Extreme Gradient Boost Classifier.

ABSTRACT: Millions of new mutations have been discovered largely due to advancements in genome projects, but characterizing their effects through traditional wet-lab experiments remains labor-intensive and time-consuming. Functional prediction algorithms offer a solution by enabling the efficient screening of mutations, thereby saving time and resources. The objective of this study was to develop a competitive algorithm for predicting the functional impact of missense mutations. A unified database and substitution matrices containing predictor variables specifically for missense mutations were initially constructed. Subsequently, values for the predictor variables were collected from the training and test sets derived from the ClinVar and HumsaVar databases. A series of supervised machine learning c

SUBMITTER: Pereira GRC 

PROVIDER: S-EPMC11886911 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature