Gene expression profiles of gastric cancer and counterpart normal tissues were analyzed with bioinformatics and machine learning methods to address the problem of discovery of biomarker genes and model of the tumor molecular diagnosis.Firstly

a support vector machine (SVM) was employed to find the feature gene subset with best classification performance for distinguishing cancerous tissues and the counterparts.And then

using genetic algorithm filter feature genes based on the classification rate of SVM.The decision tree was employed to extract rule subsets from the feature genes.These rules were tested by the test dataset with cross validation method

and the diagnosis model was constructed with these rules.The results indicate the gastric biomarkers and the diagnosis model would give more instruction in biological experiments and clinical diagnosis reference model.