Skip to Main Content
We introduce a method for modeling cancer diagnosis at the molecular level using a Chinese microarray gastric cancer dataset. The method combines an artificial neural network with a decision tree that is intended to precede standard techniques, such as classification, and enhance their performance and ability to detect cancer genes. First, we used the relief algorithm to select the featured genes that could unravel cancer characteristics out of high dimensional data. Then, an artificial neural network was employed to find the biomarker subsets with the best classification performance for distinguishing cancerous tissues and their counterparts. Next a decision tree expression was used to extract rules subsets from these biomarker sets. Rules induced from the best performance decision tree, in which the branches denote the level of gene expression, were interpreted as a diagnostic model by using previous biological knowledge. Finally, we obtained a gastric cancer diagnosis model for Chinese patients. The results show that using the Chinese gastric biomarker genes with the diagnostic model provides more instruction in biological experiments and clinical diagnosis reference than previous methods.