Application of machine learning and deep learning techniques on reverse vaccinology – a systematic literature review

Document Type

Article

Source of Publication

Soft Computing

Publication Date

1-1-2025

Abstract

Reverse vaccinology (RV) is recognized as a productive method of vaccine discovery since it may be used to create vaccines for a variety of infectious pathogens. With the potential for machine learning (ML) algorithms to enable quick and precise predictions of vaccine candidates against new infections, RV is of particular relevance. Despite the fact that ML has been used successfully in the past, Deep learning (DL) model-based RV approaches have not been used widely. DL techniques are known to provide more complicated models and better performance for AI applications. This paper supports and reviews the roles of machine learning and Deep Learning in predicting potential vaccine candidates and discovery processes. Our study involved a systematic evaluation of selected publications, identified through a combination of prior knowledge and keyword searches across freely accessible databases. A meticulous screening process, considering contextual relevance, abstract quality, methodology, and full-text content, was employed. The literature review, conducted with a rigorous methodology, encompasses a thorough analysis of articles focusing on machine learning and deep learning techniques.

ISSN

1432-7643

Publisher

Springer Science and Business Media LLC

Volume

29

Issue

1

First Page

391

Last Page

403

Disciplines

Computer Sciences

Keywords

Deep learning, Machine learning, Reverse vaccinology, Vaccine candidate prediction

Scopus ID

85219608499

Indexed in Scopus

yes

Open Access

no

Link to Full Text

Share

COinS