Clinical Bioinformatics for Early Detection and Risk Stratification in Immune-Mediated Diseases

Immune-related diseases (IRDs) represent a multifaceted field spanning autoimmune disorders, oncological conditions, and immunotherapy-linked complications. These disorders are collectively distinguished by disordered immune regulation, which can give rise to unpredictable and diverse clinical courses. Recent years have witnessed groundbreaking advances in immunotherapy, most notably immune checkpoint inhibitors in oncology, redefining treatment paradigms but simultaneously leading to new diagnostic and management challenges, such as immune-related adverse events (irAEs). Despite these innovations, early and accurate diagnosis, as well as precise risk stratification, remain elusive due to substantial patient heterogeneity and the complex interplay between genetics, immune function, and environmental factors. There is a growing consensus that refined, integrative methodologies are required to unravel the intricacies of immune dysregulation and chart disease trajectories across this spectrum. Modern studies have highlighted the power of multi-omics profiling, high-throughput data analysis, and real-world clinical evidence, but gaps persist in reliably predicting disease onset, progression, or therapeutic outcomes, particularly when translating computational findings into actionable clinical insights.This Research Topic aims to bridge the divide between computational bioinformatics and practical clinical application in immune-related diseases. The core objectives are to identify robust, early diagnostic biomarkers and to construct prediction models that can anticipate disease onset, track progression, and inform personalized interventions in both autoimmune and cancer settings. A central focus is on the application of artificial intelligence and integrative, multi-modal data analytics to not only enhance disease characterization but also to support clinical decisions, thereby translating advanced computational discoveries into improved patient outcomes. Further, the Research Topic will foster the validation and implementation of bioinformatics tools in real-world and multi-center clinical environments, addressing regulatory, ethical, and translational challenges along the way.While the research topic encompasses a broad range of diseases united by immune dysregulation, it is specifically limited to investigating computational and clinical bioinformatics approaches for early detection and risk stratification. Key themes include, but are not limited to:- Multi-omics and systems biology in immune-related diseases, including single-cell and spatial analyses- Identification and validation of early detection biomarkers across autoimmune diseases and cancer- Application of artificial intelligence and machine learning for predictive modeling and risk assessment- Patient stratification based on molecular and immune characteristics to advance precision medicine and therapeutic response prediction- Investigation of immunotherapy-related complications, especially predictive modeling and biomarker discovery for immune-related adverse events- Integration of clinical data, electronic health records, and real-world evidence with computational methods- Translational and implementation studies from computational tool development to multi-center clinical validation, along with regulatory and ethical considerations. Topic editors: Wenlin Yang, NIKEE AWASTHEE, Dan Jin Source: https://www.frontiersin.org/research-topics/79575/clinical-bioinformatics-for-early-detection-and-risk-stratification-in-immune-mediated-diseases