Abstract
The integration of multi-omics data has emerged as a transformative approach to advancing precision medicine. By combining diverse datasets such as genomics, transcriptomics, proteomics, and metabolomics, researchers are able to gain deeper insights into the complex biological networks that underlie human health and disease. This article explores the benefits and challenges of integrating multi-omics data for precision medicine, focusing on its potential to enhance disease diagnosis, treatment personalization, and therapeutic outcomes. It discusses various computational methods, including machine learning and systems biology approaches, to integrate and analyze multi-omics data, and examines the future directions of multi-omics in personalized healthcare.
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