AI-Driven Omics Research: Integrating Genomic, Proteomic and Metabolomic Data for Precision Discovery

Todd Cort

AI-Driven Omics Research: Integrating Genomic, Proteomic and Metabolomic Data for Precision Discovery

Author(s)	Todd Cort
Country	United States
Abstract	The rapid expansion of genomic, proteomic and metabolomic technologies has generated increasingly complex biological datasets that provide complementary views of biological systems. However, extracting meaningful biological knowledge from these heterogeneous datasets remains challenging because of their high dimensionality, nonlinear relationships, missing values, batch effects and differences in measurement scales. Artificial intelligence (AI), machine learning and deep learning are increasingly being used to integrate multi-omics data and accelerate biological discovery. This paper examines the emerging role of AI-driven multi-omics research in integrating genomic, proteomic and metabolomic information for precision discovery. It proposes an integrated AI-Multi-Omics Framework consisting of five interconnected stages: data generation and preprocessing, cross-omics integration, AI-based representation learning, biological interpretation and experimental validation. The paper explores applications in precision medicine, biomarker discovery, disease classification, drug discovery, therapeutic response prediction, systems biology and personalised healthcare. Particular attention is given to multimodal learning, graph-based approaches, deep learning, attention mechanisms, knowledge graphs and explainable AI. The study also examines challenges involving data heterogeneity, limited sample sizes, model interpretability, data privacy, reproducibility, computational requirements and clinical translation. The paper argues that the future of omics research will increasingly depend on integrating molecular layers rather than analysing genomic, proteomic and metabolomic data independently. AI can provide the computational capability necessary to identify relationships across these layers, but reliable discovery requires high-quality data, biologically meaningful models, rigorous validation and interdisciplinary collaboration. The paper concludes that AI-driven multi-omics has the potential to transform biological research from descriptive molecular profiling towards predictive and mechanistic precision discovery.
Keywords	Artificial Intelligence, Multi-Omics, Genomics, Proteomics, Metabolomics, Precision Discovery, Machine Learning, Deep Learning, Biomarker Discovery, Precision Medicine.
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-17

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Driven Omics Research: Integrating Genomic, Proteomic and Metabolomic Data for Precision Discovery

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