Yapay zeka güdümlü çoklu-omik entegrasyonu hassas onkolojide: veri seli ile klinik kararlar arasında köprü kurmak

Özet (abstract)

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), particularly deep learning and machine learning, bridges this gap by enabling scalable, non-linear integration of disparate omics layers into clinically actionable insights. We explore cutting-edge AI methodologies, including graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI (XAI) for transparent clinical decision support. Critical applications are highlighted, such as AI-driven therapy selection (e.g., predicting targeted therapy resistance), proteogenomic early detection, and radiogenomic non-invasive diagnostics. We further address translational challenges: data harmonization, batch correction, missing data imputation, and computational scalability. Emerging trends, federated learning for privacy-preserving collaboration, spatial/single-cell omics for microenvironment decoding, quantum computing, and patient-centric "N-of-1" models, signal a paradigm shift toward dynamic, personalized cancer management. Despite persistent hurdles in model generalizability, ethical equity, and regulatory alignment, AI-powered multi-omics integration promises to transform precision oncology from reactive population-based approaches to proactive, individualized care.

Yazarların özeti; kaynağından alınmıştır. Clinical and Experimental Medicine, 2025 · DOI ↗