Journal of Machine and Computing, Год журнала: 2025, Номер unknown, С. 768 - 788

Опубликована: Апрель 5, 2025

The rapid advancement in vaccine development has become increasingly critical addressing global health challenges, particularly the wake of emerging infectious diseases. Traditional methods design, while effective, often involve lengthy processes trial and error, which can delay deployment life-saving immunizations. In pursuit enhancing efficacy, application deep learning techniques emerged as a transformative approach. This study presents implementation an Integrated Neural Network Model (INNM), synergistically combines Artificial Networks (ANNs) Random Forests for predictive immune response optimal design. INNM employs hybrid feature selection methodology, integrating Pearson correlation with Recursive Feature Elimination (RFE), to identify most relevant immunological predictors. Implemented Jupyter Notebook environment, model achieved impressive accuracy rate 98.4%, demonstrating its potential revolutionize development. innovative approach underscores capability predict responses high precision, paving way next generation vaccines.

Язык: Английский