The integration of computational models with clinical data has significantly advanced the development of biomarkers in inflammatory bowel disease (IBD). These novel approaches rely on machine learning (ML) and artificial intelligence (AI) to process high-dimensional omics data, helping to identify key molecular signatures and improve diagnostic accuracy.1,2 ML techniques have proven particularly valuable in distinguishing between IBD subtypes, such as Crohn’s disease and ulcerative colitis, by uncovering differential gene expression patterns and protein-protein interaction networks.1
Recent studies have demonstrated the potential of these technologies to identify novel biomarkers, such as differentially expressed genes (DEGs) that are associated with IBD progression. For instance, DEGs like VWF, IL1RL1, and DENND2B have been shown to reveal strong diagnostic potential.3 These discoveries not only enhance early disease detection but also provide insights into patient-specific disease mechanisms, making personalised treatment strategies easier.2
Another promising application of computational models in biomarker development is the creation of "digital twins"—virtual patient models that predict therapeutic responses based on biomarker data and disease phenotypes.4 This approach leverages multiscale modelling, combining mechanistic and machine learning methods to simulate patient outcomes, enabling more precise dosing and treatment regimens.4,5
In conclusion, computational approaches have revolutionised the identification of clinical biomarkers in IBD. These methods offer the ability to integrate large datasets, uncover hidden molecular interactions, and predict therapeutic responses, all of which are crucial for advancing precision medicine in IBD.3,4,6
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