Comparative study of microarray data for cancer research

In comparison to traditional "single-gene" study method such as reverse transcriptase-polymerase chain reaction (RT-PCR), microarray technology can produce high-throughout gene expression data simultaneously. The advancement of this technology also presents a big challenge. In cancer research, the issue is how to identify the signature genes, or biomarkers associated with particular cancer to perform precise, objective and systematic cancer diagnosis and treatment. More specifically, the goal is how to accurately analyze and interpret the resulting large amount of gene expression data with relatively small patient sample size. As such, we have been developing a novel multischeme system that can derive optimal decision based on the best utilization of gene expression data features and clinical, and biological knowledge. In the paper, we are reporting the results of the first phase development of our novel system, to use unsupervised clustering methods to discover gene relationship and to use knowledge-based supervised classification to get highly accurate prediction in cancer diagnosis and prognosis study. This work sets up foundation for our next step drug target study.