Deep Learning for Multi-Omics Integration: Architectures, Applications and Challenges in Precision Medicine

Multi-omics is a field that studies the interactions between different biological modalities to obtain a more precise medical understanding of biological phenomena. With recent advances in high-throughput technologies, multi-omics datasets have become more accessible, enabling a better characterization of clinical observations. However, multi-omics data integration comes with many challenges, such as high dimensionality, small sample sizes, multimodality, missing omics layers, batch effects, and technical artifacts among others. The rapid evolution of deep learning presents a promising solution to address these issues. More specifically, recent advances in representation learning allow the extraction of meaningful and compact embeddings that combine information from multiple sources. Additionally, explainable predictive modeling can help uncover key features and support biomarker identification. The scope of this review is to provide a comprehensive overview of multi-omics data, focusing on integration strategies and emerging deep learning frameworks that enhance modern precision medicine, improve disease diagnosis and prognosis, support biomarker discovery and enable personalized treatment prediction. We aim to bridge the gap between molecular biology and artificial intelligence and highlight how multi-omics integration has the potential to uncover complex biological mechanisms, improve clinical decision making and contribute to a more complete understanding of disease development, progression and treatment response.