Optimization of Tumor Gene Mapping Based on Visual Communication Design

Abstract

In view of the high dimensionality of data needed to be processed by traditional tumor gene map, and the problem of redundant genes still exists, an optimization method of tumor gene map drawing based on visual communication design is proposed. Using a variety of detection methods to detect tumor genes and their products, according to the visual communication design and deep learning methods, select the appropriate tumor characteristic genes, analyze their applicability, in the low dimensional feature space representation samples, through the data set sample learning model, effectively express the level of gene difference, select the appropriate tumor characteristic genes, and achieve the optimization of tumor gene mapping. It also designs the control experiment, compares the optimized results with the traditional results, which confirm that the optimized gene map can greatly reduce the computational complexity.