Deciphering Human Disease Immunity by Harnessing Integrative Multi-Omics and Machine Learning

The field of immunology has long recognized immune system dysregulation as a cornerstone of pathogenesis across a range of human diseases, including cancers, autoimmune disorders, and chronic inflammatory conditions. Yet, despite progress, the precise regulatory mechanisms underpinning immune homeostasis, the nature of aberrant cellular interactions, and the multidimensional dynamics of microenvironments in diseased tissues remain only partially understood. Traditional investigative approaches have struggled to resolve the complex, heterogeneous landscape of immune cell function and signaling networks at single-cell and tissue levels. Recent years, however, have witnessed significant advances in high-throughput multi-omics platforms—including genomics, transcriptomics, proteomics, and epigenomics—further complemented by spatial profiling tools. Simultaneously, the application of machine learning and artificial intelligence has catalyzed the analysis of increasingly sophisticated datasets, empowering researchers to discern patterns and biological signals that would otherwise remain obscured. Despite these advances, a comprehensive integration of these approaches to elucidate the full scope of immune dysregulation across diseases is still lacking, leaving critical gaps for both basic understanding and translational application.This Research Topic aims to integrate cutting-edge multi-omics methodologies with powerful computational analyses to unravel the systemic alterations and cellular nuances driving immune dysfunction in human diseases. The primary objective is to define new immune signatures, characterize evolving cell states, and decipher the interplay between cells and their microenvironment at an unprecedented resolution. Key goals include the discovery of robust biomarkers, the unveiling of novel therapeutic targets, and the stratification of patients for precision immunomodulatory therapies, especially within cancer and immune-related diseases. By advancing the convergence of multi-omics and ML/AI frameworks, this Research Topic seeks to accelerate the interpretation of immune mechanisms underlying disease heterogeneity, progression, and therapy response, thus informing innovative clinical approaches.This Research Topic will focus on studies that employ integrative multi-omics and machine learning to explore immune processes in human pathology, prioritizing translational relevance and mechanistic depth. We are particularly interested in manuscripts that provide novel computational or experimental insights, as well as those presenting comprehensive reviews of emerging concepts. To gather further insights in disease-immunity interplay, we welcome articles addressing, but not limited to, the following themes:o Deciphering immune cell patterns and states in diverse disease contexts.o Detection and validation of immune-associated molecular interactors.o Construction of novel immune subtypes utilizing machine learning frameworks.o Integrative analyses revealing the impact of immune dysregulation via multi-omics and AI/ML.o Discovery and preclinical evaluation of new targets for immunotherapeutic interventions. Topic editors: Xinhua Liu, Tiancheng Zhang, Mostafa Rezapour, Bing Yang, Yang Li Source: https://www.frontiersin.org/research-topics/78803/deciphering-human-disease-immunity-by-harnessing-integrative-multi-omics-and-machine-learning