The research field of inflammation and immunity, critical to both health and disease, is undergoing a transformative phase with the integration of precision medicine, supported by advancements in multi-omics, computational biology, and artificial intelligence (AI). AI-driven analytics can integrate layers such as genomics, transcriptomics, proteomics, and metabolomics, allowing for the identification of individual immune-inflammation signatures. This integration is crucial for understanding how immune pathways interact with various inflammatory states, potentially revolutionizing early disease detection, therapeutic strategy tailoring, and accurate prediction of disease outcomes.
This Research Topic aims to explore how AI-powered methodologies and multi-omics technologies can be harnessed together to propel advancements in precision medicine specifically targeting inflammation and immunity cross-talk. The goal is to elucidate underlying molecular mechanisms, discover novel biomarkers, and design targeted therapies that consider the unique molecular and immunological profiles of each patient, thereby enhancing the personalization of healthcare.
To gather further insights in AI-enhanced precision medicine, we welcome articles addressing, but not limited to, the following themes:
o AI-Enhanced Multi-Omics Analysis, including integration techniques for diverse omic data and computational frameworks for comprehensive immune profiling.
o Single-Cell Omics for Inflammatory and Immune Networks to understand cellular heterogeneity and map immune cell states and transitions.
o Spatially Resolved Transcriptomics in Immune Microenvironments for detailed analysis of localized immune responses and tissue-specific interactions.
o Machine Learning and Deep Learning Approaches to develop predictive models for disease susceptibility and therapeutic outcomes.
o Transcription Factor and Signaling Networks focusing on regulatory circuits of immune and inflammatory responses.
o Precision Therapeutics and Personalized Drug Responses to optimize treatment regimens based on individual omic profiles.
o Predictive Modeling for Therapeutic Target Identification leveraging large-scale data integration.
o Synergistic Approaches and Collaborative Networks aiming for a holistic understanding through combined experimental and computational research.
Studies without a focus on immunological mechanisms or consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases that are not accompanied by robust and relevant validation, for instance in an independent patient population or by PCR, are considered out of scope of this section.
The research field of inflammation and immunity, critical to both health and disease, is undergoing a transformative phase with the integration of precision medicine, supported by advancements in multi-omics, computational biology, and artificial intelligence (AI). AI-driven analytics can integrate layers such as genomics, transcriptomics, proteomics, and metabolomics, allowing for the identification of individual immune-inflammation signatures. This integration is crucial for understanding how immune pathways interact with various inflammatory states, potentially revolutionizing early disease detection, therapeutic strategy tailoring, and accurate prediction of disease outcomes.
This Research Topic aims to explore how AI-powered methodologies and multi-omics technologies can be harnessed together to propel advancements in precision medicine specifically targeting inflammation and immunity cross-talk. The goal is to elucidate underlying molecular mechanisms, discover novel biomarkers, and design targeted therapies that consider the unique molecular and immunological profiles of each patient, thereby enhancing the personalization of healthcare.
To gather further insights in AI-enhanced precision medicine, we welcome articles addressing, but not limited to, the following themes:
o AI-Enhanced Multi-Omics Analysis, including integration techniques for diverse omic data and computational frameworks for comprehensive immune profiling.
o Single-Cell Omics for Inflammatory and Immune Networks to understand cellular heterogeneity and map immune cell states and transitions.
o Spatially Resolved Transcriptomics in Immune Microenvironments for detailed analysis of localized immune responses and tissue-specific interactions.
o Machine Learning and Deep Learning Approaches to develop predictive models for disease susceptibility and therapeutic outcomes.
o Transcription Factor and Signaling Networks focusing on regulatory circuits of immune and inflammatory responses.
o Precision Therapeutics and Personalized Drug Responses to optimize treatment regimens based on individual omic profiles.
o Predictive Modeling for Therapeutic Target Identification leveraging large-scale data integration.
o Synergistic Approaches and Collaborative Networks aiming for a holistic understanding through combined experimental and computational research.
Studies without a focus on immunological mechanisms or consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases that are not accompanied by robust and relevant validation, for instance in an independent patient population or by PCR, are considered out of scope of this section.