AI-Driven Multi-Omics Integration for Biomarker Discovery and Disease Risk Prediction

Abstract

The rapid growth of high-throughput biological data has created new opportunities for understanding disease mechanisms, identifying biomarkers, and improving precision medicine. However, biological datasets such as genomics, transcriptomics, proteomics, and metabolomics are often high-dimensional, heterogeneous, noisy, and difficult to integrate using traditional statistical methods. This paper proposes an AI-driven multi-omics integration framework for biomarker discovery and disease risk prediction. The proposed method combines deep representation learning, attention-based feature selection, and graph neural networks to capture complex relationships among genes, proteins, metabolites, and clinical phenotypes. By constructing a biological interaction graph and learning cross-omics representations, the model can identify disease-associated molecular patterns and improve predictive performance. Experimental analysis on public multi-omics datasets demonstrates that the proposed framework achieves higher accuracy, F1-score, and area under the receiver operating characteristic curve compared with conventional machine learning baselines. The results suggest that AI-based multi-omics integration can provide a powerful computational tool for discovering reliable biomarkers, supporting early diagnosis, and advancing personalized biomedical research.