SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

Journal: Frontiers in cellular and infection microbiology

PMID: 40007603

Abstract

INTRODUCTION: Accurate prediction of immunogenic proteins is crucial for vaccine development and understanding host-pathogen interactions in bacterial diseases, particularly for Salmonella infections which remain a significant global health challenge.

Authors

Ottavia Spiga

Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.
Anna Visibelli

Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.
Francesco Pettini

Department of Medical Biotechnology, University of Siena, ITALY.
Bianca Roncaglia

Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy.
Annalisa Santucci

Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.

Keywords

Algorithms Antigens, Bacterial Bacterial Proteins Computational Biology Humans Immunogenicity, Vaccine Machine Learning Proteome Salmonella Salmonella Infections Salmonella typhimurium Salmonella Vaccines Vaccine Development

External Resources

View on PubMed Access via DOI PubMed (40007603)

SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

Abstract

Authors

Keywords

External Resources

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