New and emerging technologies, including single cell immune profiling, are leading to an explosion in the generation of large-scale immunological data. Due to the current data volume, heterogeneity, and continuous flow, more and more immunological problems are becoming “big data” problems. Big data problems in immunology include, for example, the detection of signals and patterns in very noisy data sources, the integration of multi-omics immunological data, the inclusion of prior knowledge stored in public repositories, and the need for automated, interpretable decisions. To address these challenges, a new generation of computational approaches needs to be developed.
Machine learning (ML), Artificial Intelligence (AI), and data integration are at the forefront of computational methods development and are becoming fundamental tools to process complex immunological information. These tools can impact virtually all areas of immunology leading to, for example, the identification of new therapeutic targets and optimization of clinical trial design. ML/AI, combined with extensive immune and microbiome profiling, may also result in personalized predictions of health outcomes, such as the response to infection, vaccination, and immunotherapy.
This Research Topic aims to bring together researchers working in various domains of basic and applied immunology, with a common interest in ML/AI and data integration. Our article collection will include state-of-the-art approaches, highlighting common and domain-specific challenges. We welcome the submission of both Original Research and Review articles using ML/AI and data integration in the following areas:
? applications to nutritional immunology, immunometabolism, microbiome analysis
? single cell and bulk analysis of omics and multi-omics data in immune responses
? applications to drug discovery in the context of infectious and inflammatory diseases
New and emerging technologies, including single cell immune profiling, are leading to an explosion in the generation of large-scale immunological data. Due to the current data volume, heterogeneity, and continuous flow, more and more immunological problems are becoming “big data” problems. Big data problems in immunology include, for example, the detection of signals and patterns in very noisy data sources, the integration of multi-omics immunological data, the inclusion of prior knowledge stored in public repositories, and the need for automated, interpretable decisions. To address these challenges, a new generation of computational approaches needs to be developed.
Machine learning (ML), Artificial Intelligence (AI), and data integration are at the forefront of computational methods development and are becoming fundamental tools to process complex immunological information. These tools can impact virtually all areas of immunology leading to, for example, the identification of new therapeutic targets and optimization of clinical trial design. ML/AI, combined with extensive immune and microbiome profiling, may also result in personalized predictions of health outcomes, such as the response to infection, vaccination, and immunotherapy.
This Research Topic aims to bring together researchers working in various domains of basic and applied immunology, with a common interest in ML/AI and data integration. Our article collection will include state-of-the-art approaches, highlighting common and domain-specific challenges. We welcome the submission of both Original Research and Review articles using ML/AI and data integration in the following areas:
? applications to nutritional immunology, immunometabolism, microbiome analysis
? single cell and bulk analysis of omics and multi-omics data in immune responses
? applications to drug discovery in the context of infectious and inflammatory diseases
