The 6th International Symposium on Intelligent Data Analysis, Proceeding:Advances in Intelligent Data Analysis, September 8-10, 2005, Madrid, Spain
High-throughput microarray data are extensively produced to study the effects of different treatments on cells and their behaviours. Understanding this data and identifying patterns of groups of genes that behave differently or similarly under a set of experimental conditions is a major challenge. This has motivated researchers to consider multiple methods to identify patterns in the data and study the behaviour of hundreds of genes. This paper introduces three methods, one of which is a new technique and two are from the literature. The three methods are cluster mapping, Rank Products and SAM. Using real data from a number of microarray experiments comparing the effects of two very different products we have identified groups of genes that share interesting expression patterns. These methods have helped us to gain an insight into the biological problem under study.
The 6th International Symposium on Intelligent Data Analysis, Proceeding:Advances in Intelligent Data Analysis [Proceedings].