Industrial Engineering & Management

Predicting Protein Mutation Effects Using Ensemble Learning with Supervised Methods Using Large-scale Protein Language Models

Abstract

Caspian Thorne

Understanding the impact of protein mutations is vital in various scientific domains, from drug development to personalized medicine. Recent advancements in machine learning, particularly ensemble learning techniques coupled with supervised methods, have shown promise in predicting protein mutation effects. This article delves into the integration of large-scale protein language models into ensemble learning frameworks for enhanced accuracy and reliability in assessing mutation effects. By leveraging these sophisticated models, researchers can decipher intricate protein structures and anticipate the functional consequences of mutations, revolutionizing biotechnology and pharmaceutical research.