Methods of Microarray Data Analysis

Microarray experiments provide the scientific community with huge amounts of data. Without appropriate methodologies and tools, significant information and knowledge hidden in these data may not be discovered. Therefore, there is a need for methods capable of handling and exploring large data sets. The field of data mining and machine learning provides a wealth of methodologies and tools for analyzing large data sets. We review two classical machine learning techniques suitable for microarray analysis, namely decision trees and artificial neural networks. We outline how these approaches fit into a wider data mining framework.