Background: Precision medicine demands robust, reproducible biomarkers that reflect the full biological complexity of disease. Single-omics platforms capture only one molecular layer, limiting predictive power.
Objective: To evaluate multi-omics integration strategies for biomarker discovery, with emphasis on genomics, transcriptomics, proteomics, and metabolomics, and assess their translational value in precision medicine.
Methods: A systematic comparative analysis was performed on publicly available multi-omics datasets (TCGA, GTEx, CPTAC) using integration frameworks including MOFA+, SNF, and iCluster+. Machine learning models were applied for biomarker panel validation.
Results: Multi-omics integration improved biomarker identification accuracy by 18-34% relative to single-layer approaches. Area under the ROC curve exceeded 0.91 across five disease models, with stronger clinical stratification and therapeutic prediction.
Conclusion: Multi-omics frameworks significantly outperform single-layer approaches in biomarker discovery and clinical prediction, establishing a strong foundation for adoption in precision medicine workflows.