An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model

By producing the required proteins, the process of gene expression establishes the physical properties of living things. Gene expression from DNA or RNA may be recorded using a variety of approaches. Regression analysis has evolved in prominence in the area of genetic research recently. Several of the genes in high dimensional gene expression information for statistical inference may not be related to their illnesses, which is one of the major problems. The ability of gene selection to enhance the outcomes of several techniques has been demonstrated. For censored survival data, the Cox proportional hazards regression model is the most widely used model. In order to identify important genes and achieve high classification accuracy, a new technique for selecting the tuning parameter is suggested in this study using an optimization algorithm. According to experimental findings, the suggested strategy performs much better than the two rival methods in terms of the area under the curve and the number of chosen genes. This study provides a comprehensive assessment of the latest work on performance evaluation of regression analysis in gene selection. In addition to its performance analysis, this research conducts a thorough assessment of the numerous efforts done on various extended models based on gene selection in recent years.