Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Moreover, it significantly outperformed the state-of-the-art ( P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Moreover, enrichment analysis was performed to identify key signaling pathways.

The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden.

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