About this Research Topic
Advancements in omics-scale profiling technologies have ushered in new perspectives in immunology research and expanded our understanding of disease heterogeneity. However, the inherent sparsity and complexity of omics data necessitate sophisticated computational and statistical methodologies for effective analysis, modeling, integration, and interpretation to unravel the mechanisms of disease pathologies and immune responses. The field of systems immunology takes an interdisciplinary approach to facilitate the generation and testing of new hypotheses regarding immunological functions and pathways. To advance ground-breaking research in this area, cutting-edge computational methods, grounded in systems immunology and computational omics, combined with innovative experimental strategies, will be essential. These approaches are poised to translate discoveries into practical applications to translational immunology research.
Systems immunology and computational omics are rapidly advancing fields at the intersection of clinical, innovative experimental technologies, immunology, genomics, bioinformatics, and computational biology. Traditional immunological studies, utilizing the “reductionist” approach, have provided valuable insights into the components and functions of the immune system, but often fall short of capturing the full complexity of immune responses, particularly for complex diseases. This is due to the dynamic, interconnected nature of the immune system, where a myriad of cells and molecules interact in a highly specific yet variable manner depending on the disease context. Single-cell multimodal technologies can now profile individual cells at an unprecedented scale and resolution, enabling the discovery of new cell states and a more granular view of the immune response. Further, Artificial Intelligence (AI) models are evolving to uncover patterns and predict immune responses, offering insights into disease mechanisms and potential therapeutic targets. Advances in AI now allow for the integration of data across different omics platforms (genomics, transcriptomics, proteomics, etc.), leading to a more holistic understanding of the immune system. The combination of systems immunology and computational omics offers a powerful approach to understanding and manipulating the immune system for therapeutic purposes. By leveraging recent technological advances, computational methods, and experimental designs, researchers can address complex immunological challenges, leading to more effective and personalized treatments. Continued investment in these areas, along with interdisciplinary collaboration, will be key to unlocking the full potential of transformative medicine.
In this collection, we are interested in studies advancing systems immunology through the development of new computational approaches, experimental systems, or their integration, across a broad range of disease contexts. We welcome the submission of original research, review, mini review, benchmarking analysis, and perspective articles that cover the following issues, including:
• New ways using computational AI methods to integrate, interpret, and visualize immunogenomics data.
• Benchmarking approaches to evaluate the performance of cross-data modality integration, including genetics, single-cell genomics, and other multi-modal data.
• Studies that leverage integrative modeling (e.g., cross-diseases, cross-tissue compartments, or cross-species) for new biological findings.
• Models that improve the application of single-cell measurements of the immune system.
• Experimental approaches delivering insights into the immune system as a coordinated system.
• Studies that bridge experimental and computational approaches toward new hypothesis generation.
• Integrative approaches that advance immunological findings for translational and clinical impact.
• Novel machine learning approaches to model the structured immune datasets, including short time series or spatial imaging.
Topic Editor Dr. Chuangqi Wang provides a consulting service in computational immunology for Moderna. The other Topic Editors declare no conflict of interest with regard to this Research Topic.
Systems immunology and computational omics are rapidly advancing fields at the intersection of clinical, innovative experimental technologies, immunology, genomics, bioinformatics, and computational biology. Traditional immunological studies, utilizing the “reductionist” approach, have provided valuable insights into the components and functions of the immune system, but often fall short of capturing the full complexity of immune responses, particularly for complex diseases. This is due to the dynamic, interconnected nature of the immune system, where a myriad of cells and molecules interact in a highly specific yet variable manner depending on the disease context. Single-cell multimodal technologies can now profile individual cells at an unprecedented scale and resolution, enabling the discovery of new cell states and a more granular view of the immune response. Further, Artificial Intelligence (AI) models are evolving to uncover patterns and predict immune responses, offering insights into disease mechanisms and potential therapeutic targets. Advances in AI now allow for the integration of data across different omics platforms (genomics, transcriptomics, proteomics, etc.), leading to a more holistic understanding of the immune system. The combination of systems immunology and computational omics offers a powerful approach to understanding and manipulating the immune system for therapeutic purposes. By leveraging recent technological advances, computational methods, and experimental designs, researchers can address complex immunological challenges, leading to more effective and personalized treatments. Continued investment in these areas, along with interdisciplinary collaboration, will be key to unlocking the full potential of transformative medicine.
In this collection, we are interested in studies advancing systems immunology through the development of new computational approaches, experimental systems, or their integration, across a broad range of disease contexts. We welcome the submission of original research, review, mini review, benchmarking analysis, and perspective articles that cover the following issues, including:
• New ways using computational AI methods to integrate, interpret, and visualize immunogenomics data.
• Benchmarking approaches to evaluate the performance of cross-data modality integration, including genetics, single-cell genomics, and other multi-modal data.
• Studies that leverage integrative modeling (e.g., cross-diseases, cross-tissue compartments, or cross-species) for new biological findings.
• Models that improve the application of single-cell measurements of the immune system.
• Experimental approaches delivering insights into the immune system as a coordinated system.
• Studies that bridge experimental and computational approaches toward new hypothesis generation.
• Integrative approaches that advance immunological findings for translational and clinical impact.
• Novel machine learning approaches to model the structured immune datasets, including short time series or spatial imaging.
Topic Editor Dr. Chuangqi Wang provides a consulting service in computational immunology for Moderna. The other Topic Editors declare no conflict of interest with regard to this Research Topic.
Keywords: systems immunology, Computational AI, Omics Modelling, Translational Medicine
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