AI-Driven Multi-Omics Integration for Precision Medicine: Linking Genomic, Proteomic, and Clinical Data for Improved Healthcare Outcomes

Abstract

The high throughput in the development of omics technologies has been followed by a surge in the amount of biological data that has never been translated into viable medical use. Precision medicine aims at personalizing the approach to healthcare to individual patients through the combination of molecular and clinical data. Artificial Intelligence (AI) and machine learning (ML) in this context have scalable solutions to managing and interpreting multi-omics datasets. This paper explores AI-based methods to combine genomic, proteomic and clinical information to enhance diagnostic quality and therapy decisions. Its methodology includes a conceptual framework integrating genomic variants, protein expression profiles and patient clinical data and preprocessing steps such as normalizing features and missing data treatment. Deep learning autoencoders and graph neural networks, as well as federation learning frameworks, are emphasized as AI models that can model a complex biological interaction and be privacy preserving. Oncology, cardiology and neurology case studies demonstrate that multi-omics integration is beneficial in better classifying disease subtypes, predicting risks, and forecasting tolerance to treatments. One example of this is that mutational signatures with proteomic markers outperform single-omics models in subtyping cancer but integrated proteogenomic models have potential in predicting heart failure progression. The results highlight the fact that AI-mediated multi-omics combination has the potential to revolutionize the health practice by facilitating individualistic treatment approaches. In order to achieve the full potential of precision medicine, future studies need to overcome barriers to model interpretability, data standardization, and clinical implementation.