This review explores patient-centric omics mining strategies for target identification in disease mechanism-centric medicine. Using chronic kidney disease (CKD) as an illustrative example, the paper proposes a data-driven and unbiased patient stratification approach to support traditional classification based on observable clinical symptoms and diagnoses. Advocating for state-of-the-art systems biology, the paper suggests integrating transcriptomic, clinical, and morphological data to construct verifiable models of diseases like CKD. These models can provide a framework for mechanistic analysis and for the identification of potential therapeutic targets in the context of precision medicine.
