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

## TL;DR

XGBMut is a new tool that predicts the impact of genetic mutations using machine learning, making it faster and easier to analyze mutations without complex setups.

## Contribution

XGBMut is a novel machine learning algorithm for predicting missense mutation functionality with a standalone, user-friendly interface.

## Key findings

- XGBMut achieved high accuracy in classifying the functional impact of missense mutations.
- XGBMut outperformed ten existing prediction algorithms in performance evaluations.
- The tool is accessible without requiring web servers or third-party software.

## 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 classifiers were then trained, and their performance
was evaluated using the test set. The best-performing model was additionally
compared against ten currently available functional prediction algorithms.
The proposed algorithm, XGBMut, demonstrates exceptional accuracy
in classifying missense mutations while also exhibiting a competitive
performance. Additionally, a user-friendly graphical interface was
developed to enhance accessibility for professionals in various fields.
Unlike most existing methods, XGBMut eliminates the need for a web
server dependency and the installation of third-party software, making
it a more versatile tool for users.