UMCA-Net: Uncertainty-aware multi-stage cross-attention for cost-aware multi-omics data classification

Abstract

Multi-omics data integration holds great promise for precision medicine, yet its clinical adoption is hindered by high acquisition costs and the complexity of heterogeneous data representations. To address these challenges, we propose an uncertainty-aware multi-view dynamic decision framework for efficient and trustworthy disease classification. Unlike conventional static fusion strategies, our approach leverages evidential deep learning grounded in Dempster-Shafer theory to explicitly disentangle predictive confidence from epistemic uncertainty, enabling cost-sensitive and progressive inference. Specifically, omics modalities are introduced adaptively, such that additional data are only acquired when the current evidence is insufficient to support a reliable decision. At the core of the UMCA-Net, a Transformer-based multi-stream architecture with global joint cross-attention captures rich cross-modal interactions and produces Dirichlet-based evidential representations. This design allows principled uncertainty quantification and supports dynamic decision-making. We evaluate the proposed method on four benchmark multi-omics datasets (ROSMAP, LGG, BRCA, and KIPAN). Experimental results demonstrate that our model achieves state-of-the-art performance while significantly reducing data acquisition requirements. Notably, in certain cohorts, over 90% of samples can be confidently classified using only low-cost initial modalities without compromising accuracy. Overall, this work provides a scalable and practical solution for balancing diagnostic accuracy and economic cost, facilitating the deployment of multi-omics models in real-world clinical settings. Our code is available to the public at github.com/chenzhao2023/UMCA-Net.