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

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Artificial intelligence and chemometrics are transforming multi-omics data integration for biological insights. AI methods, including deep learning, offer advanced solutions for complex data challenges, improving clinical applications.

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Area of Science:

  • * Computational Biology and Bioinformatics
  • * Analytical Chemistry
  • * Artificial Intelligence in Life Sciences

Background:

  • * Multi-omics data integration is crucial for understanding complex biological systems.
  • * Traditional chemometric methods face challenges with high-dimensional, heterogeneous omics data.
  • * Recent advancements (2020-2025) focus on AI-driven approaches for robust data fusion.

Purpose of the Study:

  • * To critically review AI-driven methods for multi-omics data integration.
  • * To evaluate the evolution of chemometrics towards deep learning architectures.
  • * To assess clinical applications and future directions in the field.

Main Methods:

  • * Review of deep learning architectures: Convolutional Neural Networks (CNNs), Autoencoders (AEs), Variational Autoencoders (VAEs), Graph Neural Networks (GNNs).
  • * Exploration of Explainable AI (XAI) frameworks like SHAP and LIME for interpretability.
  • * Analysis of vertical and horizontal integration strategies, including attention mechanisms and network-informed architectures.

Main Results:

  • * AI and deep learning methods show significant capabilities in non-linear feature extraction and data fusion.
  • * AI-driven multi-omics integration achieved 20%-30% performance improvements in clinical applications (Alzheimer's, obesity, cancer).
  • * Emerging hyphenated techniques and miniaturized analyses are advancing the field.

Conclusions:

  • * AI, particularly deep learning, offers powerful tools for overcoming multi-omics integration challenges.
  • * Explainable AI is essential for clinical translation and analytical chemistry applications.
  • * Future research should focus on scalability, privacy-preserving methods (federated learning), and advanced applications like spatial multi-omics.