SDM-Q: Reinforcement Learning Framework Reduces Multi-Omics Data Acquisition Costs While Maintaining Diagnostic Accuracy
Researchers have proposed SDM-Q, a deep Q-learning framework that adaptively decides which omics data modalities to acquire for disease classification, minimizing unnecessary testing. The system reformulates multi-omics diagnosis as a sequential decision problem, balancing classification accuracy against the cumulative cost of acquiring molecular data. The approach could improve the practicality of precision medicine workflows by reducing expensive and redundant omics profiling in clinical settings.
SDM-Q (Staged Decision-Making with Q-learning) is a reinforcement learning framework designed to address a key inefficiency in precision medicine: most existing deep learning models require complete multi-omics profiles at inference time, even when fewer data modalities may suffice for accurate diagnosis. The framework treats diagnosis as a finite-horizon sequential decision process, where an action-value function determines at each stage whether to acquire an additional omics modality or terminate and output a prediction. Rewards are assigned only at the terminal stage and reflect both classification correctness and cumulative acquisition cost, encouraging the model to reach accurate diagnoses with minimal data. A backward stage-wise optimization strategy is introduced to improve training stability and policy consistency across stages. Experiments on four public datasets — ROSMAP, LGG, BRCA, and KIPAN — showed that over 99% of BRCA subjects and over 95% of KIPAN subjects achieved accurate classification using just a single omics modality, while average modality acquisition remained below two for ROSMAP and LGG. The results were competitive with methods that use full multi-omics inputs, suggesting that adaptive, cost-aware decision-making can substantially reduce redundancy without sacrificing diagnostic performance.
What's missing
The study relies exclusively on public benchmark datasets and does not include prospective clinical validation or real-world cost data for omics modality acquisition. It is unclear how the framework generalizes to omics modalities or disease types beyond those tested, and the computational requirements for deploying the RL policy in a clinical setting are not discussed. The paper is a preprint and has not yet undergone formal peer review.
What different sources said
- arXiv cs.LGCenter
SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning
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