← Volver a resultados
Ficha bibliográfica · Consulta y acceso
Artículo

From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges

Nicole Simone De Lima Coelho et al · Frontiers Media S.A · 2026

Acceso abierto disponible
Lectura rápida. Revisá los datos básicos del recurso y luego accedé al contenido desde el botón principal. En esta ficha solo se muestra la información necesaria para identificar la obra, citarla y abrirla.

Acceso al recurso

Entrá al contenido desde la opción principal o elegí otra fuente disponible.

Acceso principal

Acceso abierto disponible

Recurso identificado como acceso abierto, sin confirmar automáticamente si es texto completo directo.
Abrir recurso

Resumen

Descripción general del contenido del recurso.

Although vaccination remains one of the most impactful interventions in contemporary public health, with consistent and widely documented reductions in morbidity and mortality, the traditional empirical development of vaccines—largely based on trial-and-error strategies—still reflects an incomplete understanding of the complexity underlying immune responses. With the emergence of systems immunology, supported by multi-omics technologies, mathematical modeling, and computational tools, vaccinology has progressively incorporated the integration of multiple biological layers, addressing critical mechanistic gaps and mitigating limitations of the classical model, thereby transitioning from an empirical framework toward a predictive and integrative paradigm. In this context, the present review critically examines how systems immunology contributes to rational vaccine design by exploring its technological foundations—particularly omics approaches—discussing strategies for data integration, analyzing translational implications, and incorporating considerations related to Artificial Intelligence (AI), regulatory governance, ethics, and global equity. Within this evolving landscape, systems vaccinology has demonstrated promising results and optimistic perspectives, particularly regarding predictive capacity, immunological stratification, vaccine personalization, and potential epidemiological impact. At the same time, challenges including reproducibility concerns, risk of overfitting, the distinction between multi-omic correlation and functional causality, the need for longitudinal and experimental validation, algorithmic bias, excessive reliance on computational models, and regulatory barriers to the approval of data-driven vaccines represent important limitations of this approach. Taken together, these considerations indicate that systems immunology constitutes not merely a technological refinement but a genuine paradigm shift, redefining vaccine development as a predictive, iterative, and integrative process that must be scientifically validated and ethically contextualized, with profound implications for global public health.

Cómo citar

Elegí el formato que necesitás y copiá la referencia al portapapeles.

APA 7

al, N. S. D. L. C. E. (2026). From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges. https://doi.org/10.3389/fsysb.2026.1819469

MLA

al, Nicole Simone De Lima Coelho et. "From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges." 2026. https://doi.org/10.3389/fsysb.2026.1819469.

Chicago

al, Nicole Simone De Lima Coelho et. 2026. "From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges.". https://doi.org/10.3389/fsysb.2026.1819469.

Harvard

al, N. S. D. L. C. E. 2026, From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges, Frontiers Media S.A, available at: https://doi.org/10.3389/fsysb.2026.1819469 [Accessed 28 Jun. 2026].

Compartir e imprimir

Guardá la ficha, copiá su enlace permanente o imprimila como PDF.

Exportar referencia

Si usás un gestor bibliográfico, podés exportar el registro en los formatos más comunes.

Detalles del recurso

Información bibliográfica útil para confirmar que se trata del material correcto.

Título
From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges
Autor / colaboradores
Nicole Simone De Lima Coelho et al
Editorial
Frontiers Media S.A
Año de publicación
2026
ISSN
2674-0702
ISSN
2674-0702
Idioma
eng

Materias

Explorá otros recursos relacionados a partir de estas materias.

Copiado