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

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