Deep learning for biomarker discovery in heterogeneous data from autoimmune and inflammatory conditions

Autoimmune and inflammatory diseases encompass a wide spectrum of complex conditions characterized by dysregulated immune responses, multifactorial etiology, and overlapping clinical manifestations. Traditional biomarker discovery approaches have struggled to capture the heterogeneity inherent in these disorders due to variability across genomic, transcriptomic, proteomic, and clinical phenotypic layers. In recent years, deep learning has emerged as a powerful tool capable of extracting high-level representations from large, multi-modal biomedical datasets, making it particularly well-suited for biomarker discovery in complex disease landscapes. This paper explores the application of deep learning frameworks—such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders—for identifying robust and interpretable biomarkers across diverse datasets derived from patients with autoimmune and inflammatory conditions including rheumatoid arthritis, systemic lupus erythematosus, and inflammatory bowel disease. We present a comparative analysis of deep learning architectures for feature extraction, dimensionality reduction, and classification tasks, and assess their ability to integrate multi-omics data with electronic health records (EHRs) to improve diagnostic and prognostic accuracy. In addition, we propose an explainable AI (XAI) pipeline to enhance interpretability of identified biomarkers and link them to disease pathways, drug targets, and patient stratification strategies. Case studies demonstrate how deep learning accelerates discovery of non-invasive biomarkers and reveals previously undetected molecular patterns associated with disease activity and treatment response. The paper concludes with a discussion on ethical considerations, data harmonization challenges, and future directions in deploying deep learning as a clinical decision support tool in immunological diseases.