About this Research Topic
This Research Topic is the third volume of the “Towards Precision Medicine for Immune-Mediated Disorders: Advances in Using Big Data and Artificial Intelligence to Understand Heterogeneity in Inflammatory Responses” Community Series. Please see Volume I here, and Volume II here
The ability to understand the underlying mechanisms that drive the heterogeneity in inflammatory responses among patients suffering from immune-mediated conditions is critical. The nature of the immune mechanism ranges from the degree of immune cell activation, to the HLA genotypes, and the intensity of responses of target tissues to cytokines. High throughput experiments have generated significant information for us to facilitate the identification of disease-associated components for different complex immune-mediated conditions (e.g. psoriasis, lupus, rheumatoid arthritis, inflammatory bowel diseases), and the advances in biotechnologies and efficiency in computational resources have further promised cost-effective opportunities to simultaneously study the different aspects in the basic and translational domains of these human diseases. The critical questions we face are to provide efficient inference to integrate these datasets, and translate what we have learned from the disease-associated components to understand the heterogeneity (e.g. ethnic, gender, genetic differences) in disease manifestation and prognosis of these immune-mediated conditions. Translating and integrating big data to understand the intrinsic factors that shape the different unique natures of inflammation processes require an efficient and flexible framework to incorporate signatures of different types.
This Research Topic is intended to present and discuss technological, methodological, and conceptual advancements in immunological researches utilizing big data and/or new techniques to study the heterogeneity in immunological responses and clinical observations of immune-mediated disorders. We welcome the submission of Original Research, Systematic Review, Methods, Mini Review, Perspective, and Review articles, with potential topics including, but not limited to:
1. Designing efficient multi-omics studies for studying the pathophysiology of immune-mediated conditions
2. Advanced approaches to provide accurate and efficient HLA typing
3. Development of a generalizable and efficient framework to translate high throughput experimental data to study the variations in inflammatory responses
4. Multi-omics studies to unravel the signal transduction in the cytokine network
5. Providing a comprehensive understanding of the similarities and distinct biological features across different subgroups of patients suffering from immunological disorders
6. Applications of multi-omics to advance precision medicine in immune-mediated diseases
7. Novel in silico approaches to integrate omics information with electronic health record data
8. Machine learning tools to assess prognosis using patient’s healthcare records and/or -omic profiles
The ability to understand the underlying mechanisms that drive the heterogeneity in inflammatory responses among patients suffering from immune-mediated conditions is critical. The nature of the immune mechanism ranges from the degree of immune cell activation, to the HLA genotypes, and the intensity of responses of target tissues to cytokines. High throughput experiments have generated significant information for us to facilitate the identification of disease-associated components for different complex immune-mediated conditions (e.g. psoriasis, lupus, rheumatoid arthritis, inflammatory bowel diseases), and the advances in biotechnologies and efficiency in computational resources have further promised cost-effective opportunities to simultaneously study the different aspects in the basic and translational domains of these human diseases. The critical questions we face are to provide efficient inference to integrate these datasets, and translate what we have learned from the disease-associated components to understand the heterogeneity (e.g. ethnic, gender, genetic differences) in disease manifestation and prognosis of these immune-mediated conditions. Translating and integrating big data to understand the intrinsic factors that shape the different unique natures of inflammation processes require an efficient and flexible framework to incorporate signatures of different types.
This Research Topic is intended to present and discuss technological, methodological, and conceptual advancements in immunological researches utilizing big data and/or new techniques to study the heterogeneity in immunological responses and clinical observations of immune-mediated disorders. We welcome the submission of Original Research, Systematic Review, Methods, Mini Review, Perspective, and Review articles, with potential topics including, but not limited to:
1. Designing efficient multi-omics studies for studying the pathophysiology of immune-mediated conditions
2. Advanced approaches to provide accurate and efficient HLA typing
3. Development of a generalizable and efficient framework to translate high throughput experimental data to study the variations in inflammatory responses
4. Multi-omics studies to unravel the signal transduction in the cytokine network
5. Providing a comprehensive understanding of the similarities and distinct biological features across different subgroups of patients suffering from immunological disorders
6. Applications of multi-omics to advance precision medicine in immune-mediated diseases
7. Novel in silico approaches to integrate omics information with electronic health record data
8. Machine learning tools to assess prognosis using patient’s healthcare records and/or -omic profiles
Keywords: multi-omics, heterogeneity, inflammatory responses, translational research, immune-mediated diseases
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
