A Novel Hybrid Approach to Selecting Marker Genes for Cancer Classification Using Gene Expression Data

Selecting a subset of marker genes from thousands of genes is an important topic in microarray experiments for diseases classification and prediction. In this paper, we proposed a novel hybrid approach that combines gene ranking, heuristic clustering analysis and wrapper method to select marker genes for tumor classification. In our method, we firstly employed gene filtering to select the informative genes; secondly, we extracted a set of prototype genes as the representative of the informative genes by heuristic K-means clustering; finally, employed SVM- RFE to find marker genes from the representative genes based on recursive feature elimination. The performance of our method was evaluated by AML/ALL microarray dataset. The experimental results revealed that our method could find very small subset of marker genes with minimum redundancy but got better classification accuracy.