VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study aims to develop and deploy a robust tool for accura...

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Veröffentlicht in:	Immunogenetics (New York) 2025-12, Vol.77 (1), p.8
Hauptverfasser:	T, Dhanushkumar, G, Sunila B, Hebbar, Sripad Rama, Selvam, Prasanna Kumar, Vasudevan, Karthick
Format:	Artikel
Sprache:	eng
Schlagworte:	Allergology Biomedical and Life Sciences Biomedicine Cancer Cancer immunotherapy Cancer vaccines Cancer Vaccines - immunology Cancer Vaccines - therapeutic use Cell Biology Cell culture Computational Biology - methods Epitopes Epitopes, T-Lymphocyte - immunology Gene Function Histocompatibility Antigens Class I - immunology Histocompatibility Antigens Class II - immunology Human Genetics Humans Immunogenicity Immunology Immunotherapy Immunotherapy - methods Learning algorithms Lymphocytes T Machine Learning Major histocompatibility complex Neoplasms - immunology Neoplasms - therapy Original Article Predictions Software Tissue typing Vaccine development Vaccines
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