Omics Strategies and Big Data: Transforming Precision Medicine and Systems Biology
TL;DR
This review explores how integrating genomics, transcriptomics, proteomics, and metabolomics provides a holistic view of complex biological systems and addresses persistent challenges including data heterogeneity, privacy concerns, and computational bottlenecks.
Abstract
The convergence of multi-omics strategies and big data analytics is transforming modern healthcare by shifting medicine from a reactive discipline to a proactive, personalized system. This review explores how integrating genomics, transcriptomics, proteomics, and metabolomics provides a holistic view of complex biological systems. While current literature heavily documents theoretical models, a persistent gap remains in translating these high-dimensional architectures into validated healthcare workflows. We highlight the critical role of big data infrastructure, specifically cloud computing and artificial intelligence (AI), in processing massive datasets and we benchmark our approach against existing reviews to emphasize the path toward routine clinical deployment. Through concrete case studies in oncology and metabolic disorders, we illustrate the potential clinical utility of these technologies in advancing precision medicine. Finally, we address persistent challenges including data heterogeneity, privacy concerns, and computational bottlenecks, and discuss future directions required to translate multi-omics insights into routine clinical practice.