Artificial intelligence in multi-omics data integration: Advancing precision medicine, biomarker discovery and genomic-driven disease interventions

Hassan Ali *

Department of Computer Science, Maharishi International University, USA.
 
Review
International Journal of Science and Research Archive, 2023, 08(01), 1012-1030.
Article DOI: 10.30574/ijsra.2023.8.1.0198
Publication history: 
Received on 07 January 2023; revised on 18 February 2023; accepted on 21 February 2023
 
Abstract: 
The integration of multi-omics data—encompassing genomics, transcriptomics, proteomics, and metabolomics—has revolutionized biomedical research, offering unprecedented insights into disease mechanisms and therapeutic interventions. However, the complexity and volume of multi-omics datasets present significant analytical challenges that traditional computational methods struggle to address. Artificial Intelligence (AI), particularly deep learning and neural networks, has emerged as a powerful tool to overcome these limitations by enabling advanced data integration, biomarker discovery, and personalized treatment strategies. This paper explores the role of AI-driven multi-omics data integration in enhancing disease prediction, early diagnosis, and precision medicine. By leveraging AI models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and transformers, researchers can analyze complex biological interactions, identify patterns indicative of disease onset, and stratify patient populations for tailored treatment approaches. Additionally, AI-powered feature selection methods facilitate the identification of disease-specific biomarkers across multiple omics layers, paving the way for more effective targeted therapies. Moreover, AI plays a crucial role in pharmacogenomics by predicting individualized drug responses, optimizing dosage regimens, and minimizing adverse drug reactions. Machine learning algorithms, including reinforcement learning and generative models, enable real-time modeling of drug-gene interactions, leading to safer and more efficacious therapeutic interventions. Despite the transformative potential of AI in multi-omics data analysis, challenges such as data standardization, model interpretability, and ethical considerations must be addressed to ensure reliability and clinical applicability. This paper provides a comprehensive review of AI-driven multi-omics research, highlighting current advancements, challenges, and future directions in precision medicine.
 
Keywords: 
AI-Driven Multi-Omics Integration; Precision Medicine; Deep Learning in Biomarker Discovery; Genomic Disease Prediction; AI In Pharmacogenomics; Personalized Treatment Strategies
 
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0