AI RESEARCH
SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning
arXiv CS.LG
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ArXi:2605.31014v1 Announce Type: new Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine. However, acquiring complete multi-omics profiles is expensive and time-consuming, while most existing deep learning methods assume full modality availability during inference, resulting in substantial redundancy and limited practicality in clinical settings.