Graph designs for deep learning-based multi-omics integration

Modern sequencing technologies can now capture multiple omic layers from the same biological system, but integrating these views into a coherent model is far from trivial. Graph-based deep learning has become an attractive strategy because it can represent complex molecular interactions and sample relationships in a flexible way. In this review, we survey how graphs are constructed and used in multi-omics deep learning models, organizing methods by node schema, edge semantics, interaction type, integration strategy, graph context, and model architecture across bulk, single-cell, and spatial settings. We summarize the strengths and weaknesses of different design choices in terms of interpretability, data requirements, robustness to noise and missing modalities, and suitability for tasks ranging from prediction to mechanism-oriented discovery. Based on these insights, we outline a general, practical pipeline for constructing, curating, and evaluating graphs that can serve as a starting point for new multi-omics studies.