Classification Network of Gastric Cancer Construction based on Genetic Algorithms and Bayesian Network

One of the most important link in improves diagnostic accuracy and disease cure rate is accurate classification of disease. The current gene chip´s development and widely applications making the diagnosis based on tumor gene expression profiling expected to be on a fast and effective clinical diagnostic method. But the sample of gene is small and the expression data is multi-variable. In this article, we uses three data sets on gene expression profiles of gastric cancer for the construction of classification model, First, screened the gene which significantly changed in expression pattern, and use these genes as a set of the feature to reduce the number of variables, and then using genetic algorithms and bayesian network model to build the classifier, the build process uses these three gene expression data to learn classifier. Classification accuracy is calculated by leave-one cross-validation (LOOCV) and it reached 99.8%. Last we use the GO and pathway to analysis the classifier´s network structure.