Selecting dissimilar genes for multi-class classification, an application in cancer subtyping

Background: Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker identification, which are expected to be closely related to the disease. A most important task of these identified genes is that they can be used to construct a classifier which can effectively diagnose disease and even recognize the disease subtypes. Binary classification, for example, diseased or healthy, in microarray data analysis has been successful, while multi-class classification, such as cancer subtyping, remains challenging. Results: We target on the challenging multi-class classification in microarray data analysis, especially on the cancer subtyping using gene expression microarray. We present a novel class discrimination strength vector to represent individual genes and introduce a new measurement to quantify the class discrimination strength difference between two genes. Such a new distance measure...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Zhipeng Cai, Randy Goebel, Mohammad R. Salavatipour, Guohui Lin
• Keywords: BMCBI 2007, Class Discrimination Strength, Gene Expression Microarray, Microarray Data Analysis