aceAI-powered multi-omics integration for precision oncology clinical decision-making
Source article: AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions
Cancer's staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81-0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particu…
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By November 2025, this peer-reviewed review synthesized how artificial intelligence bridges the cancer multi-omics data deluge to clinical decisions, integrating genomics, transcriptomics, proteomics, metabolomics and radiomics using deep learning, graph neural networks, transformers, and explainable AI.
It matters because improved integration could shift oncology from reactive population-based care to proactive individualized care via better early detection and therapy selection, but uncertainty remains around generalizability, data harmonization, ethical equity, and regulatory alignment needed for routine clinical adoption.
- Review covers integration of genomics, transcriptomics, proteomics, metabolomics and radiomics for cancer care
- AI methods discussed include graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI for transparent decision support
- Applications highlighted include therapy selection predicting targeted therapy resistance, proteogenomic early detection, and radiogenomic non-invasive diagnostics
- Emerging trends noted are federated learning for privacy-preserving collaboration, spatial/single-cell omics, quantum computing, and N-of-1 models
AI-driven multi-omics integration improves diagnostic and prognostic accuracy in precision oncology, with recent integrated classifiers reporting AUCs around 0.81-0.87 for difficult early-detection tasks.
AI-driven multi-omics integration in precision oncology faces translational challenges including data harmonization, batch correction, missing data imputation, computational scalability, and limited model generalizability.
The rundown
The review details AI methodologies for integration, including graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI for transparent clinical decision support, applied to therapy selection, proteogenomic early detection, and radiogenomic diagnostics.
It frames translational bottlenecks as data harmonization, batch correction, missing data imputation, and computational scalability, while pointing to federated learning, spatial and single-cell omics, quantum computing, and patient-centric N-of-1 models as emerging directions toward dynamic personalized management.
Performance gains depend on rigorous preprocessing and external validation, and models face persistent generalizability, ethical equity, and regulatory alignment hurdles.
Sources
- Peer-reviewedClinical and Experimental Medicine2025-11-21
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