Marker Gene Selection of Gastric Cancer Subtype Based on Multi Microarray Data Sets

Using machine learning methods to analyze microarray data of gastric cancer and discover novel marker gene can provide suggestion for further study of the molecular mechanism, gene level diagnosis and treatment, of gastric cancer.Most existing methods use machine learning methods to extract marker gene using only one data set.This paper proposed a hybrid genetic algorithm (GA)/support vector machine (SVM) approach to analyze multi gastric cancer microarray dataset in parallel and select marker genes.Three datasets are analyzed.The experiment was performed 4 580 times.The top 20 genes with highest occurrence times in the final populations of the GA (the occurrence times can represent the significance of classification in a sense) are selected as marker genes.Based on these genes the classification accuracies are above 90% in each of the three datasets.Meanwhile, biological significance analyses show that this method can identify the tumor related genes efficaciously.These genes are vital for human gastric cancer diagnosis and classification.