AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions

The convergence of artificial intelligence and chemometrics has revolutionized multi-omics data integration, enabling unprecedented insights into complex biological systems. This critical review examines AI-driven approaches for integrating genomics, proteomics, metabolomics, and other omics layers, emphasizing developments from 2020 to 2025. We explore fundamental multi-omics challenges including batch effects, high dimensionality, and structural heterogeneity, evaluating how classical chemometric methods have evolved into sophisticated deep learning architectures. Convolutional neural networks, autoencoders, variational autoencoders, and graph neural networks demonstrate remarkable capabilities for non-linear feature extraction and data fusion. Explainable AI frameworks including SHAP and LIME address interpretability concerns critical for analytical chemistry. We review vertical and horizontal integration strategies, highlighting transformer-based attention mechanisms and biological network-informed architectures. Clinical applications in Alzheimer's disease, obesity, and cancer demonstrate 20%-30% performance improvements over traditional approaches. Emerging hyphenated techniques coupling microfluidics with mass spectrometry enable miniaturized analyses. Persistent challenges include computational scalability, overfitting mitigation, regulatory validation gaps, and interdisciplinary collaboration barriers. Future directions encompass federated learning for privacy-preserving analyses, quantum computing applications, and single-cell spatial multi-omics at subcellular resolution. This assessment provides analytical chemists with critical evaluation of available tools, benchmarking strategies, and roadmaps for advancing precision medicine and analytical applications.