SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning
arXiv:2605.31014v1 Announce Type: new Abstract: 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. To address this issue, we propose SDM-Q, a reinforcement learning framework for adaptive and cost-aware mu
The increasing complexity and cost of multi-omics data acquisition necessitate more efficient and practical classification methods, especially as AI models grow in capability and clinical applications expand.
This development allows for more cost-effective and adaptive diagnostic and classification tools in precision medicine by reducing the need for complete, expensive multi-omics profiles.
The paradigm shifts from requiring full modality availability for AI inference to adaptive, cost-aware decision-making, making advanced diagnostic tools more accessible and practical in clinical settings.
- · Precision Medicine Providers
- · Patients (reduced cost diagnostics)
- · AI/ML Healthcare Developers
- · Biotech and Pharma
- · Traditional diagnostic methods reliant on full data
- · Labs with high multi-omics processing costs
Reduced healthcare costs for advanced diagnostics and improved accessibility for patients.
Accelerated development and adoption of AI-driven personalized treatment plans due to practical data acquisition.
Potential for new business models around modular, on-demand multi-omics analysis, shifting diagnostic power dynamically.
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Read at arXiv cs.LG