Small-Sample-Size Data-Driven Early Disease-Detection and Re-stabilization for mRNA-Protein Gene Regulatory Networks

This paper addresses the challenges of using High-Dimensional Low-Sample-Size (HDLSS) data set for early disease detection and re-stabilization in mRNA-protein gene regulatory networks. We demonstrate that detecting the pre-disease stage of mRNA-protein gene regulatory networks is possible using only the HDLSS data of either mRNA or protein. After detection, it is crucial to prevent disease progression at that point. This prevention can be achieved by enhancing the system’s stability, a process called re-stabilization. We demonstrate that the key nodes for re-stabilization in the mRNA-protein gene regulatory network manifest as mRNA- protein duals. The intervention strategy, whether suppression or promotion, is identical for each key dual’s mRNA and protein components. We further present a Lyapunov equation-based system identification method to estimate the system matrix using the HDLSS data set. The key nodes for re-stabilization can be identified from the estimated system matrix.