Immunology | Open call for papers
➔ Computational and Machine Learning Approaches in Immunology
➔ Multi-Omics Integration for Advancing Biomedical Research
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About the Editor
Dr Stefano Cacciatore
Associate Editor for Immunology
Cancer Genomic Group
International Centre for Genetic Engineering and Biotechnology (ICGEB) | Cape Town, South Africa
ORCID | Scopus Author ID | Google Scholar ID | About the Editor
Journal information
Scimago - Journal & Country Rank | Journal rank 2024: 0.660
Scopus | 2024 (as of 5/1/25): 4.6
About this journal
Publisher information
AboutScience
Multi-Omics Integration for Advancing Biomedical Research
The integration of multi-omics data has emerged as a transformative strategy in biomedical research. By capturing the complexity of biological systems across multiple layers this perspective holds promise for precision medicine, systems biology, and translational applications that bridge research and clinical practice.
We invite researchers and clinicians to contribute to this topic, which aims to explore methodologies, applications and challenges of multi-omics integration in advancing healthcare and life sciences.
Topics of interest include (but are not limited to):
- Human Health: Applications of multi-omics in precision medicine, biomarker discovery, drug development, and personalized therapeutic strategies.
- Computational Approaches: Novel algorithms, machine learning frameworks, data harmonization strategies, and integrative pipelines for handling multi-omics complexity.
- Disease-Specific Insights: Multi-omics studies applied to cancer, neurodegenerative diseases, infectious diseases, autoimmune conditions, and rare diseases.
- Translational and Clinical Integration: Case studies demonstrating the utility of multi-omics in clinical decision-making, diagnostics, and patient stratification.
- Future Perspectives: Ethical considerations, data sharing policies, and frameworks to advance reproducibility, interoperability, and accessibility in multi-omics research.
This new section seeks to foster interdisciplinary collaboration and highlight the potential of multi-omics integration as a powerful tool for unraveling biological complexity and driving innovations in health research and clinical practice.
Computational and Machine Learning Approaches in Immunology
Advances in computational methods and machine learning are reshaping the field of immunology, enabling researchers to decode the complexity of immune responses at unprecedented scale and precision. By leveraging large datasets—from single-cell sequencing to clinical records—computational approaches allow for predictive modeling, biomarker identification, and the discovery of novel therapeutic strategies. These tools hold promise for bridging basic immunology with translational applications, paving the way toward precision immunology and data-driven medicine.
Topics of interest include, but are not limited to:
- Algorithmic Innovation: Development of machine learning, deep learning, and AI-driven models tailored for immunological data.
- Immune System Modeling: Computational simulations of immune responses, host–pathogen interactions, and vaccine design.
- Big Data Integration: Approaches for analyzing and harmonizing large-scale datasets such as single-cell omics, imaging, and clinical data.
- Translational Applications: Predictive models for disease progression, therapeutic response, patient stratification, and immunotherapy optimization.
- Ethical and Practical Perspectives: Challenges in data sharing, reproducibility, interpretability, and the integration of computational tools into clinical workflow
By bridging disciplines, computational and machine learning approaches are reshaping how immune complexity is understood and are driving new advances in health research and clinical practice.
