Metric for Measuring the Effectiveness of Clustering of DNA Microarray Expression

Background: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances; hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecul...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Raja Loganantharaj, Satish Cheepala, John Clifford
• Keywords: Algorithm, Biological Processes, BMCBI 2006, Pearson Correlation Distances