The rapid advancement in vaccine development has become increasingly critical in addressing global health challenges, particularly in the wake of emerging infectious diseases. Traditional methods of vaccine design, while effective, often involve lengthy processes of trial and error, which can delay the deployment of life-saving immunizations. In the pursuit of enhancing vaccine efficacy, the application of deep learning techniques has emerged as a transformative approach. This study presents the development and implementation of an Integrated Neural Network Model (INNM), which synergistically combines Artificial Neural Networks (ANNs) and Random Forests for predictive immune response and optimal vaccine design. The INNM employs a hybrid feature selection methodology, integrating Pearson correlation with Recursive Feature Elimination (RFE), to identify the most relevant immunological predictors. Implemented in a Jupyter Notebook environment, the model achieved an impressive accuracy rate of 98.4%, demonstrating its potential to revolutionize vaccine development. This innovative approach underscores the capability of deep learning to predict immune responses with high precision, paving the way for the next generation of vaccines.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Saranya K R, Josephine Usha L, Valarmathi P and Suganya Y;
Methodology: Saranya K R and Josephine Usha L;
Software: Josephine Usha L and Valarmathi P;
Data Curation: Valarmathi P and Suganya Y;
Writing- Original Draft Preparation: Saranya K R and Josephine Usha L;
Visualization: Saranya K R, Josephine Usha L, Valarmathi P and Suganya Y;
Investigation: Valarmathi P and Suganya Y;
Supervision: Saranya K R and Josephine Usha L;
Validation: Valarmathi P and Suganya Y;
Writing- Reviewing and Editing: Saranya K R, Josephine Usha L, Valarmathi P and Suganya Y;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
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Saranya K R
Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India.
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Cite this article
Saranya K R, Josephine Usha L, Valarmathi P and Suganya Y, “Next-Generation Vaccines: Leveraging Deep Learning for Predictive Immune Response and Optimal Vaccine Design”, Journal of Machine and Computing, pp. 768-788, April 2025, doi: 10.53759/7669/jmc202505061.
