Gene Expression Studies with DGL Global Optimization for the Molecular Classification of Colon Cancer

The study attempts to analyze multiple sets of genes simultaneously, for an overall global solution to the gene's joint discriminative ability in assigning tumors to known classes. With the workable concepts and methodologies described here an accurate classification of the type and seriousness of cancer can be made. The colon cancer microarray data can be classified 100% correctly without previous knowledge of their classes. The classification processes are automated after the gene expression data being inputted. Instead of examining a single gene at a time, the DGL method can find the global optimum solutions and construct a multi-subsets pyramidal hierarchy class predictor containing up to 23 gene subsets based on a given microarray gene expression data collection within a period of several hours. An automatically derived class predictor makes the reliable cancer classification and accurate tumor diagnosis in clinical practice possible.